Accurate prediction of root growth and related resource uptake is crucial to accurately simulate crop growth especially under unfavorable environmental conditions. We coupled a 1D field-scale crop-soil model running in the SIMPLACE modeling framework with the 3D architectural root model CRootbox on a daily time step and implemented a stress function to simulate root elongation as a function of soil bulk density and matric potential. The model was tested with field data collected during two growing seasons of spring barley and winter wheat on Haplic Luvisol. In that experiment, mechanical strip-wise subsoil loosening (30–60 cm) (DL treatment) was tested, and effects on root and shoot growth at the melioration strip as well as in a control treatment were evaluated. At most soil depths, strip-wise deep loosening significantly enhanced observed root length densities (RLDs) of both crops as compared to the control. However, the enhanced root growth had a beneficial effect on crop productivity only in the very dry season in 2018 for spring barley where the observed grain yield at the strip was 18% higher as compared to the control. To understand the underlying processes that led to these yield effects, we simulated spring barley and winter wheat root and shoot growth using the described field data and the model. For comparison, we simulated the scenarios with the simpler 1D conceptual root model. The coupled model showed the ability to simulate the main effects of strip-wise subsoil loosening on root and shoot growth. It was able to simulate the adaptive plasticity of roots to local soil conditions (more and thinner roots in case of dry and loose soil). Additional scenario runs with varying weather conditions were simulated to evaluate the impact of deep loosening on yield under different conditions. The scenarios revealed that higher spring barley yields in DL than in the control occurred in about 50% of the growing seasons. This effect was more pronounced for spring barley than for winter wheat. Different virtual root phenotypes were tested to assess the potential of the coupled model to simulate the effect of varying root traits under different conditions. Plants require their root systems for multiple reasons, including water and nutrient uptake from soil, and anchoring  phenotypic plasticity in response to climate change and predicted increase of both interseasonal and intraseasonal variability  may be crucial for maintaining agricultural productivity in the future  and with subsoil (deep) loosening (DL). With the simulation-based approach, we aim to (i) simulate the effect of soil strength on root growth patterns of spring barley and winter wheat, (ii) improve our understanding on how often and under which weather conditions subsoil loosening leads to positive yield effects using weather scenarios, and (iii) elucidate the potential of the coupled model to simulate the effect of different virtual root phenotypes on RLD and yield.In our study, we applied a 1D field-scale crop model coupled with a 3D root architecture model to simulate spring barley and winter wheat root and shoot growth based on field data of The Campus Klein-Altendorf (CKA) Research Facility of the University of Bonn, Germany is located in Rheinbach near Bonn . The soil on the study site was classified as Haplic Luvisol  derived from loess and is characterized by a silty clay loam texture with clay accumulation in the subsoil between about 45- and 95-cm soil depth. At that site, no pronounced plow pan or subsoil compaction by heavy machinery was observed. The climate at the experimental station can be described as temperate humid with maritime influence. The mean annual air temperature and precipitation (from 2008 to 2020) are 10.5°C and 601 mm (source: homepage CKA), respectively.2) in CF1-1 and 20 m × 3 m (60 m2) in CF1-2. The block-design experiment with the first main crop spring barley  followed by winter wheat  started in 2017 in CF1-1. In the following year, winter wheat was grown in CF1-1 and spring barley was grown in CF1-2. In 2018/19, winter wheat was grown in CF1-2.Central field experiment 1 (CF1) has a total size of 1.5 ha. The main aim of the experiment was to test the effects of strip-wise subsoil loosening (DL) and, in some treatments (not considered here), the incorporation of an organic material into this strip from 30- to about 60-cm soil depth on crop growth , which were followed by the three winter wheat cultivars Milaneco, Trebelir, and Capo in 2019/20. The cultivars Sydney, Capo, and Milaneco were selected because of presumed higher root growth in deeper soil layers compared to the other cultivars with more shallow root systems. In this study, we present field data of three growth periods of spring barley  and winter wheat  with focus on the first 2 years (CF1-1 and CF1-2).2.Field site preparation, which was conducted in autumn before sowing of spring barley , included removal of weeds, followed by primary and secondary soil tillage . All the−1), 27 April 2018 (70 kg ha−1), and 27 February and 15 April 2019 (50 kg ha−1 each). Winter wheat received 50 kg ha−1 of N on 27 February and 15 April 2019.According to soil sampling, there was no need for fertilization with phosphorous or potassium. Pests were controlled with pesticides according to standard grower practice.In CF1-1, spring barley was sown on 27 March and harvested on 25 July 2017, and winter wheat was sown on 23 October 2017 and harvested on 17 July 2018. In CF1-2, spring barley was sown on 9 April and harvested on 24 July 2018, and winter wheat was sown on 16 October 2018 and harvested on 8 August 2019. In CF1-3, the three spring barley cultivars were sown 29 March and harvested on 30 July 2020. The three winter wheat cultivars were sown on 18 October 2019 and harvested on 20 July 2020. In all the years, row spacing and within row distances were 12.5 and 3 cm. Spring barley flowering was observed around 12 June 2017, around 18 June 2018, and around 17 June 2019. Winter wheat flowering was observed by the end of May in all the 3 years. A nitrogen (N) fertilizer  was applied based on soil sampling and analysis of topsoil N concentrations. N fertilization on spring barley took place on 17 March 2017  in the control treatments  and the DL treatments “at strip” . Total carbon and nitrogen contents were measured by dry combustion (HEKAtechEuroEA 3000). All measurements were performed with at least two analytical replicates. An additional replicate was analyzed when the difference between analytical replicates exceeded 0.5 mg C gThe field data collected during the growth period and presented in this study include mean values of root length density (RLD), shoot biomass, and dry matter grain and straw yield at harvest from the regions of interest “at the strip” of the DL and the control treatment. The RLDs of spring barley were investigated four times in 2017, twice in 2018, and only once for winter wheat around flowering in 2018 and in 2019 applying the profile wall method Böhm, , a methoAround flowering in 2018, the average root diameter of spring barley and winter wheat was observed based on soil samples. For that, the soil monoliths used to convert RLD from profile walls to absolute RLD were soaked in tap water and washed. Subsequently, roots were sorted out by hand, filtering out smallest particles and dead roots, scanned and analyzed with software WinRHIZO version Pro 2017a 64 bit.−1 each), dried until constant weight, and weighed. In the melioration treatment, samples were taken both directly above the deep loosening strip and 50 cm next to the melioration strip in an undisturbed area. Dry matter straw yield and grain yield after threshing were determined. Note that part of the observations  were already published in Jakobs et al.  are described in subsequent sections.The 1D field-scale modeling platform Scientific Impact assessment and Modeling PLatform for Advanced Crop and Ecosystem (SIMPLACE) management is written in Java and contains various submodels called SimComponents for simulating the crop growth and development including crop phenology, root growth, soil water dynamics, crop nitrogen, crop water demand, and subsequent abitotic stresses . The SimComponent LintulWaterStress  factor, which is calculated according to the equation:where TRANRF is the transpiration reduction factor calculated in the SimComponent LintulWaterStress.Both factors are dimensionless, TRANRF ranges between 0 and 1 and FRTMOD is equal or >1. The root fraction provided in the partitioning table (FRTTB) is then multiplied with FRTMOD, thereby increasing the amount of assimilates transferred to the roots in the event of moderate to severe drought stress (TRANRF <0.5). Other fractions are then reduced equally to ensure that the sum of all the fractions remains equal to 1. Moreover, the model accounts for the senescence of stems and roots.−1). Assimilates provided by the shoot or the seeds are, in the first place, used for the growth of seminal roots, which determine the vertical penetration rate into the soil. In addition to the supply of assimilates by the shoots, the vertical penetration rate of seminal roots depends on soil temperature, soil dryness, and soil strength, which reduce the crop-specific maximum daily elongation rate. If the demand for assimilates by the seminal roots is lower than the supply by the shoots, the remaining assimilates determine the development of lateral roots.The SimComponent SlimRoots, a conceptional root model implemented into SIMPLACE based on Addiscott and Whitmore , estimatFor calculating water uptake in SlimWater, a so-called of root restriction factor (FRR) is calculated considering RLD and root age:where RDA4AGE depends on the age of the roots and root length density (RLD) in layer i withi. YOUTH is the days when roots are considered to be active .where RLAGE is the age of the roots in days in layer CRootbox is a functional-structural root architecture model that simulates root growth according to growth mechanisms including root elongation, branching, orientation, and senescence , t is time (day), k is the maximum root length (cm), and re is the initial daily root elongation rate (cm day−1). This elongation rate may additionally be reduced because of local soil environmental conditions such as high penetration resistance as well as because of carbon availability.where Soil penetration resistance, which varies greatly with soil water status, was modeled as a function of soil water content and bulk density based on a nonlinear soil strength function  is the soil penetration resistance, γ (Mg m−3) is the bulk density, and θ is the volumetric soil water content (cm3 cm−3). The constants a, b, and c were set to the default values 0.00587, 8.0772, and −4.65  and matric potential (h) for every defined time and soil depth. For simplicity, we assume that these stresses combine linearly  as a function of both soil strength t, z is the total stress reduction function for RE due to mechanical (Qp) and hydric (h) stresses in each time (t) and soil depth (z). RE rate can slow down because of soil strength, with an exponential decrease for a soil without continuous macropores. α(Qp) is the stress reduction function by soil strength and is given by Eq. 10 for a soil without continuous macropores  in cm day−1, is defined asRE rate, as affected by soil matric potential, h) is a dimensionless function of soil water pressure head, and REmax (cm−1) is the maximal possible RE rate without restrictions ] from 1 (maximum root elongation) to zero (no growth). The shape of this function for RE follows the concept proposed by Feddes et al. . Daily root biomass increment from SIMPLACE (LintulBiomass) was converted into maximal daily RE, which is input into CRootbox. The actual RLD derived from CRootbox' simulated root system was input into SIMPLACE. Because of the coupling, the root biomass provided by SIMPLACE determined the maximal root elongation (feedback loop). When potential RE is higher than maximal, CRootbox reduces root growth equally. Decreased root length density may result in decreased soil water and nutrient uptake and increased water/nutrient stress in SIMPLACE, which may reduce total biomass production. Note that potential root growth limitation due to soil physical stresses takes place before potential root growth limitation due to biomass provided by the shoot. In each time step, the amount of carbon potentially available for root growth and the amount of carbon needed for root growth  are first computed independently from each other. If the available carbon is less than the required carbon, root growth is further restricted by limiting each root equally in its growth until the available and the required carbon demands match. If the required carbon is less than the available carbon, roots can fulfill their growth unlimited from carbon constraints, and excess carbon is absorbed by the shoots. Thus, the simulated actual root length density depends on soil bulk density, soil matrix potential, and C allocated from the shoots to the roots.The model solution of SIMPLACE-CRootbox was used to simulate spring barley (2017 and 2018) and winter wheat (2017/18 and 2018/19) root and shoot growth for two growing periods per crop (CF1-1 and CF1-2). The simulation runs started a few weeks before sowing: on 1 October for winter wheat and on 1 March for spring barley of the respective years. Initial volumetric soil water content was set to 25% (0–30 cm) and 30% (30–210 cm) in spring before sowing of spring barley and to 20% after the very dry summers before sowing of winter wheat. The observed soil texture, soil bulk density, and soil organic carbon content were used to estimate the soil hydraulic parameters using the pedotransfer function HYPRES  field data of only one growth period per cultivar were available, model calibration and validation were not possible. The data of CF1-3 were, thus, used to evaluate the effect of different genotypes on root growth and yield in 2019 (spring barley) and 2019/20 (winter wheat) in both treatments. The model was used to test diverse virtual root phenotypes by changing root parameters systematically (see below) in these two growing seasons. The cultivar trial (CF1-3) was, thus, used to assess the results of the simulation study testing diverse root phenotypes (see below), but the cultivars grown in these years were not simulated explicitly.The tree parameters r , ln , and lmax  of the default CRootbox root parameters for winter wheat published in Morandage et al. , 2021b aModel runs under varying weather conditions at that site and, in a second step, for varying cRootbox parameters representing diverse root phenotypes were conducted. The simulation results are not directly comparable to field data but aim to explore the potential of subsoil loosening and the capability of the coupled model to simulate diverse root phenotypes.To evaluate the effect of subsoil loosening under various weather conditions, simulation runs for both treatments using the SIMPLACE-CRootbox model were conducted. For that, the 12 years where daily weather data were available from the CKA Research Facility (2008–2019) were applied.Oyiga et al.  evaluateAll scenario simulations started on 1 March (spring barley) and 1 October (winter wheat) each year with the same initial conditions as described above and in The strip-wise subsoil loosening in treatment DL reduced soil bulk density by about 16% (in 30- to 45-cm soil depth) compared to the control treatment . Note thAt the Campus Klein-Altendorf Research Facility , the season in 2017 can be characterized as a normal growth period with abundant precipitation, whereas the main spring and summer growth period in 2018 was very dry, and in 2019 it was dry and very hot . The cumIn general, the strip-wise deep loosening fostered higher RLD of spring barley and winter wheat in most soil depths at the strip (DL treatment) as compared to the control treatment . The soiIn CF1-3, the observed RLD from 0 to 30 cm was highest for the shallow rooting spring barley cultivar Salome, whereas from 30- to 100-cm soil depth, it was highest for the deep rooting cultivar Eunova . Furthermore, the RLD observed at flowering was higher in DL than in the control, especially from about 20 to 70 cm soil depth. In 2020, about 30% lower RLD below 30 cm and, in particular, from 60 to 180 cm soil depth of the shallow rooting winter wheat cultivar Trebelir compared to the deeper rooting cultivars Milaneco and Capo was observed .−1 in the control treatment. Thus, for the spring cereal cultivated in 2018, the enhanced RLD due to strip-wise deep loosening paid off.In 2018, winter wheat yield level was very low mainly because of the drought, which caused emergency ripening . In all −1 (control), and mean winter wheat grain yield was 6.4 (DL) and 6.2 t ha−1 (control) (−1) in 2019, but the difference was not statistically significant. Similarly, the deep rooting winter wheat cultivar Milaneco had lower yield in the DL treatment compared to the control (6.8 vs. 7 t ha−1) in 2020, but the difference was, again, not significant. The Grain yield of the deep rooting winter wheat cultivar Capo was 14% higher (nonsignificant) in the DL treatment as compared to the control (6.9 vs. 5.9 t ha−1). Thus, there was no clear trend for an impact of deep loosening on yield and AGB of deep or shallow rooting cereal cultivars in 2019/20.In CF1-3 where three cultivars with presumed differences in deep rooting characteristics were tested, grain yield did not differ significantly: mean spring barley grain yield was 4.8 (DL) and 4.9 t hacontrol) . The dee−1. Three CRootbox parameters  were fitted to better represent the observed root growth dynamics over depth and time , the enhanced absolute RLD in the DL treatment compared to the control was hardly reflected . In 2017 control . The sim−3 (control) for spring barley (−3 (control) for winter wheat . Besides, simulated spring barley AGB and yield were slightly higher in 2017 in the DL treatment than in the control. For winter wheat, no differences in grain yield and AGB between the treatments were simulated. In 2017, higher spring barley RLD in the DL treatment than in the control between the 50th day after sowing and flowering was simulated  for that site. The mean yield gain due to deep loosening was only 2% with an impact on grain yield in four out of the 12 years. Only in one out of the 12 years, namely in the growth period of 2011/12, a yield gain due to deep loosening of 24% was achieved. In this season, maximal rooting depth in the end of March was higher for DL than for the control (87 vs. 72 cm). In that season, only about 22% of the mean precipitation in March was observed, which retarded rooting in spring in the control treatment. Thus, in years with very dry spring periods, deep loosening has paid off.In the case of winter wheat, the low initial soil moisture at sowing imitating a very dry summer resulted in rather low winter wheat yield level in several years . Here, the mean simulated grain yield of winter wheat and spring barley was 5.1 and 5.4 t haOyiga et al.  reportedWe varied three CRootbox parameters for testing the effect of different virtual but plausible phenotypes. Hereby we first increased and then decreased the parameter value of each parameter in sequence while keeping the other parameters constant and evaluated the simulated RLD and yield. −1 for winter wheat and from 5.4 to 6.6 t ha−1 for spring barley. The grain yield of virtual phenotypes of both cereals was similar for most situations, although spring barley yield was lower in the control than in the DL treatment. In the case of the control, a high spacing between lateral branches as well as a low initial tip elongation rate decreased winter wheat grain yield by about 0.1 t ha−1. The greatest grain yield difference was simulated in spring barley in the control treatment for varying initial tip elongation rate values. Spring barley yield was about 0.2 t ha−1lower for the phenotype with a low initial tip elongation rate for the default and enhanced elongation rate phenotypes (control). Also, low number of maximal basal roots reduced spring barley grain yield by about 0.1 t ha−1 (control). No yield differences for varying virtual phenotypes were found in the DL treatment. Although the mean RLD was more than 30% higher for the virtual phenotype with the high spacing between lateral branches than for the phenotype with default value  and 4.7 t ha−1(r = 5) in the control. The yield difference due to DL increased from 8% (r = 9) to 10% (r = 7) to 13% (r = 5).The parameter with the greatest influence on yield , initial−1 as moderate rooting barriers and soil bulk densities above 1.82 g cm−1 as severe barriers for rooting. With values ranging from 1.20 to 1.61 g cm−3, the soil bulk densities of our field are even lower than the threshold for moderate rooting barrier. However, roots elongate more slowly in dry soils (with enhanced soil strength) because of a combination of drought stress and mechanical impedance . In 2018, the level of winter wheat yield was very low because of the dry spell and heat the cereals faced in that growth period, which caused emergency ripening.Although the strip-wise DL led to enhanced RLD of both kinds of cereal in most soil depths at the strip in the DL treatment as compared to the control, no significant yield effects were observed. Similarly, such compensatory root growth behavior in the loose soil was observed by Pfeifer et al. . The autIn a meta-analysis, Schneider et al.  evaluatevia the profile wall method and the monolith factor is also not exact and RLD depends not only on penetration resistance but also on other small-scale factors  that were not simulated, these deviations are acceptable. Thus, model simulations can be conducted to improve our understanding of how often and under which conditions subsoil loosening leads to positive yield effects at that site. The simulations from 2017 to 2019 at the experimental site indicate that the pronounced early root development of spring barley due to reduced soil strength in the DL treatment was favorable for crop growth at least in years with drought stress. This was also observed in the first sampling date in 2017 from 0 to 60 cm soil depth. These results highlight the importance of early root development of spring cereals.The effect of a given level of compaction is related to both weather and crop species, since soil strength is a function of soil compaction and soil moisture Batey, . The effSimilar simulated root biomass but higher cumulative RLD in the DL compared than in the control treatment points to thinner roots with smaller root diameters in treatment DL especially in 2018 . In 2018The root phenotype is very plastic and influenced by numerous interactions. Since metabolic costs of enhanced root growth are relatively high, breeding for genotypes having an increased allocation of resources to roots may carry negative consequences for yield, especially in resource-poor environments Lynch, . Thus, iWe varied three CRootbox parameters for testing the effect of different virtual but plausible phenotypes close to our field observations on simulated root and shoot growth. By simulation-based testing of different root phenotypes with various RLD distributions over soil depth, we were able to quantify the effects of different virtual root phenotypes as well as of subsoil loosening. The most sensitive parameter was initial tip elongation rate r and, again, highlights the importance of early root development of spring cereals.CRootbox is capable of representing the behavior of different root phenotypes, which is a clear improvement compared to SIMPLACE-SlimRoots. Such models may support future root phenotyping by indicating the importance of specific root phenes. They will also help to better understand the benefit of root phenotypic plasticity as a breeding target for crops in variable environments  implemented for CRootbox. With the SIMPLACE-CRootbox model, the best of both worlds was combined: the more detailed 3D root model provides information on root traits while it benefits from the consideration of the soil-plant-atmosphere system  and the computational efficiency of the 1D soil-crop model.via carbon allocation to the roots as well as simulation of yield. This hybrid 1D/3D model also overcomes the drawback of the extensive computational effort of fully 3D functional-structural plant (FSP) models . In agreement with Postma et al. . However, such an approach has to be combined with real experiments for model calibration and validation.We conclude that such 1D/3D models are feasible tools to assess the value of physiological traits in targeted environments and for specific management  for informed choice of cultivars or to focus research on promising root traits  of the Federal Ministry of Education and Research (BMBF), Germany, and by Deutsche Forschungsgemeinschaft  under Germany's Excellence Strategy-EXC 2070-390732324.DL was employed by Simulationswerkstatt. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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Contouring of anatomical regions is a crucial step in the medical workflow and is both time-consuming and prone to intra- and inter-observer variability. This study compares different strategies for automatic segmentation of the prostate in T2-weighted MRIs.This study included 100 patients diagnosed with prostate adenocarcinoma who had undergone multi-parametric MRI and prostatectomy. From the T2-weighted MR images, ground truth segmentation masks were established by consensus from two expert radiologists. The prostate was then automatically contoured with six different methods: (1) a multi-atlas algorithm, (2) a proprietary algorithm in the Syngo.Via medical imaging software, and four deep learning models: (3) a V-net trained from scratch, (4) a pre-trained 2D U-net, (5) a GAN extension of the 2D U-net, and (6) a segmentation-adapted EfficientDet architecture. The resulting segmentations were compared and scored against the ground truth masks with one 70/30 and one 50/50 train/test data split. We also analyzed the association between segmentation performance and clinical variables.The best performing method was the adapted EfficientDet (model 6), achieving a mean Dice coefficient of 0.914, a mean absolute volume difference of 5.9%, a mean surface distance (MSD) of 1.93 pixels, and a mean 95th percentile Hausdorff distance of 3.77 pixels. The deep learning models were less prone to serious errors (0.854 minimum Dice and 4.02 maximum MSD), and no significant relationship was found between segmentation performance and clinical variables.Deep learning-based segmentation techniques can consistently achieve Dice coefficients of 0.9 or above with as few as 50 training patients, regardless of architectural archetype. The atlas-based and Syngo.via methods found in commercial clinical software performed significantly worse (0.855 Segmenting anatomical regions of interest (ROIs) such as organs and lesions in medical images is a standard procedure in many medical workflows. In radiotherapy, accurate segmentation of both lesions and surrounding organs in MRI images is needed to calculate the radiation dose and estimate the risk of normal tissue complications. In prostate fusion biopsy, segmentation is crucial to guide the collection of the cancerous specimen. Typically, the ROIs are manually or semi-automatically hand-drawn by trained medical personnel in treatment planning systems, but may suffer from poor reproducibility and/or accuracy –4. In adThe current state-of-the-art models for automatic image segmentation are predominantly deep learning (DL)-based. These are typically characterized by heavy use of convolutional operations, which enables the models to successively and automatically extract relevant features at different resolutions and locations in the images. The most famous and widely applied such model is the U-net , which wAlthough advancements are continuously being made within the field, the automatic segmentation methods employed in common treatment planning and patient monitoring software tend to lag behind relative to the most recent results in the literature. This may be part of the reason manual segmentation is still the method of choice in most clinics. At present, it is not well known how the segmentation algorithms in proprietary medical software compare against recent DL advances.To facilitate the integration of automatic segmentation models into clinical practice, it will be important to analyze how and when automatic segmentation is appropriate. For instance, patients with exceptionally large or small prostate volume may be particularly hard for them to segment, which would call for human intervention. Other factors such as age or the severity of the patient’s condition may also play an important role. It is also critical to try to gauge how much patient data is needed to train the segmentation models to an acceptable standard so that clinics can readily devote the appropriate amount of resources. In the current autosegmentation literature, these types of considerations are usually left out.We compare the segmentation performance of four common DL segmentation models and training strategies: a V-net (a 3D evolution of the popular U-net), a transfer learning approach with weights pretrained on the ImageNet dataset, a generative adversarial network (GAN), and a 3D version of the EfficientDetB0 architecture adapted for segmentation.We benchmark our models against two retail medical software tools: Syngo.via  and Raystation 9B .We investigate whether clinical patient parameters such as risk class, prostate volume, and prostate specific antigen (PSA) levels have any impact on the segmentation performance. Such data could help institutions and clinics with defining personalized protocols to indicate whether or not specific patients are suitable for automatic segmentation.Lastly, we repeat our experiments with different amounts of training data  in order to evaluate how impactful and important the size of the data set is when training clinical segmentation models.In this work, we compare four automatic DL segmentation models on a single data set consisting of 100 T2-weighted MRI scans of prostate cancer patients. We also benchmark the DL models against multi-atlas segmentation and a proprietary segmentation algorithm in the Syngo.via clinical imaging software developed by Siemens Healthineers. The models were chosen to cover some of the most essential training strategies  and architectures . Our main contributions can be summarized as follows:This study was conducted on a set of 100 T2-weighted MRI scans of the prostate from cancer patients at IEO European Institute of Oncology IRCCS, Milan, Italy. All patients gave their consent for use of their data for research and educational purposes, and the use of the data was approved by the local Ethical Committee, which waived the requirement for further consent specific to this study. The images were acquired using a 1.5 T scanner , mean surface distance (MSD), and 95th-percentile Hausdorff distance (HD95) to the reference standard (ground truth). The scores are defined as follows:We compared the performance of six different automatic segmentation models (described in detail in the following section): Dice coefficient is a measure of the overlap between two geometrical objects, defined by:A and B are the sets of pixels of the objects being compared. Dice coefficients range from 0 to 1, where 1 corresponds to perfectly overlapping objects, and 0 corresponds to any configuration where their intersection is zero.The ARVD is the absolute volume difference calculated relative to the volume of the ground truth (in this case B), defined by:MSD: The MSD (measured in pixels) between the surfaces A and B, is defined asHD95: the 95th percentile Hausdorff distance between two geometrical objects A and B is defined by:We compared the models in terms of their segmentation performance and variance. We also tested if the best-performing model was significantly outperforming the other models using the Wilcoxon signed-rank test.We analyzed the performance of the methods in terms of the most poorly segmented patients. Since the segmentations are made on images from real patients, it is of great importance to not jeopardize any downstream implications resulting from unacceptable segmentations.We tested if the methods systematically overestimate or underestimate the volume by calculating the signed relative volume difference and measuring its statistical deviation from unity (Wilcoxon signed-rank test).In order to find potential confounding factors that may have a significant impact on the quality of the automatically generated contours, we looked at associations between the results and different clinical variables . If found, this could serve to indicate whether a given prostate is suitable for automatic segmentation. It may also be useful for constructing better segmentation algorithms in the future. This analysis was made with the Kruskal-Wallis tests , and Spearman correlation (for continuous variables). This analysis was only performed on the Dice and MSD performance metrics.Lastly, we tested whether the increased training size in the 70/30 split significantly improved the performance of the best model compared to the 50/50 split. This can indicate whether datasets of this size are adequate to train a model, or if larger datasets are needed. The performance increase resulting from adding data will diminish at some point, and understanding this interplay will be important for future studies, especially because good-quality clinical data is hard to collect. This analysis was done with the Kruskal-Wallis test (since samples were of different size). As an extension of this analysis, we also performed a pair-wise Wilcoxon signed rank test on all patients in the intersection of the 70/30 and 50/50 test sets .Since the control of Type I error with multiple comparison corrections leads to an increase in Type II errors , no FDR corrections were applied in the above analyses. This reduces the probability of discarding potential associations in this explorative study  is a common learning approach when data or resources are scarce or otherwise tainted . The idea is to apply pre-trained weights trained on a much larger dataset and/or a broader task in order to save computing resources and leverage the robustness of previously learned features. Hence, it’s a convenient choice in medical image analysis where data is often sparse.Our TL approach consisted of a U-net decoder stacked on top of an EfficientNetB0 encoder Fig.  pre-traiTo transform the GANs are a family of models where two network agents—one generator and one adversarial/critic/ discriminator—compete against each other with a shared objective function. The generator is trained to generate “fake” samples (in our case segmentation masks) with the aim of trying to fool the discriminator into thinking they are real, while the discriminator is trained to distinguish fake/generated samples from real ones. As the generator gets better at generating realistic-looking samples, the discriminator has a harder time identifying them as fake. And when the discriminator improves its discrimination performance, the generator needs to generate more realistic-looking samples in order to keep up, ideally leading to a self-improving feedback loop. One compelling feature of GANs is that their objective function is implicit in the architecture, leading to a type of qualitative optimization.Our GAN implementation was based on the pix2pix  frameworEfficientDet  is an arThe standard EfficientDet model consists of an EfficientNet backbone see Fig.  and a seTo adapt the network to 3D, the The number of filters in the The fast normalized fusion was applied over channels and inputs (instead of just inputs).The expansion factor in the MBConvs was set to 2.0 (instead of 6.0).The upscaling was done by a nearest-neighbor resizing followed by a The EfficientDet architecture can be modified for segmentation with just minor changes. While the original model applied multiple BiFPN layers in succession, we noticed that stacking BiFPNs deteriorated the performance in the focused and relatively simple task of prostate segmentation. Instead, our implementation applies the fast normalized fusion technique from the BiFPN layer directly to the outputs of the EfficientNet backbone see Fig. . This grk cross-entropy loss function with k most poorly segmented pixels. The batch size was set to 2 in all cases due to memory constraints. To fully leverage the available data, we heavily augmented the samples with the following augmentation methods (listed in order of application):Mixup , 23 withHorizontal flips with 50% probability.Uniform random rotation in the Random bilinear resize and translation with a uniformly distributed scale factor in the  range.Elastic deformation , i.e. to The models were trained with a modified pixel-wise top-entclass1pt{minimaWe used the Adam optimizer with default learning parameters (D(x) by x and For the GAN, which uses separate optimizers for the generator and the discriminator, we instead set the parameters to GAN loss , definedmentclass2pt{minimAll models were implemented with TensorFlow 2.4 in Python 3.7 and trained on an NVIDIA Tesla V100-SXM2 (16 GB).For Dice, the TL model was the only one to perform at a similar level.All DL-based models performed similarly in terms of ARVD.In terms of MSD, the GAN approach and the TL approach (in the 50/50 split) achieved similar performance.For HD95, only the 70/30 split with the TL approach achieved similar performance.The variance of the segmentation results was fairly small, particularly for the DL models  of the different methods are displayed in Fig. In terms of performance of the most poorly segmented patients Fig. , the EffThe mean relative volume produced by the different models are displayed in Table No significant associations  with just three exceptions: the MSD and the HD95 for the V-net and the HD95 for the GAN. However, the differences between the two splits were not statistically significant  were similar . In this project, we tried to commit roughly equal amounts of effort to all models such that the results would reflect the underlying differences and not just tuning variability. But since GANs are notoriously hard to train and stabilize, it is likely that the GAN model could be improved further by a more careful and thorough exploration. A similar argument can be made regarding the TL model and its possible backbones, model structures  indicates no significant performance increase when expanding the training set by 40% from 50 to 70 images. However, in 13 out of the 16 combinations of DL-based models and metrics, the performance was better in the 70/30 split on average. It is likely that this dataset was simply too small to reveal the difference with significance. Increasing the size of the dataset does not only improve the average performance, but also the models’ resistance to outliers; the scores of the 50/50 split generally exhibit higher variances and more severe outliers.When analyzing the relationship between clinical variables and segmentation performance, one can conclude that exceptionally large prostate volume and old age may lead to poor segmentation performance, although this result was only significant for the MSD metric. In principle, a positive linear correlation between MSD and Volume also implies a good performance (i.e. low MSD) for patients with small volumes, although exceptionally small prostates are likely to cause poor performance due to other effects. Since there was only one observed significant relationship out of four performance metrics, it is likely that clinical variables are not strong predictors of segmentation performance.The images used in this study all came from a homogeneous population, and all patients had relatively severe cancer (Gleason score 6 or above). While it is crucial to know that segmentation works well for these cases, it would be helpful to add data from healthy patients so that they can also be segmented effortlessly. If the intention of the algorithms is to exclusively handle patients within this population, a relatively small data set  may suffice. But as soon as results on external samples are of interest , it will be critical to add additional data to represent these. For the same reason, it is not surprising that the Siemens’ segmentation model in the Syngo.via software had the worst performance by far.k MSE), learning rates & schemes , optimizers , normalizations (instance norm), dropout strategies , activation functions . In addition, we tested several alternative block designs and miscellaneous settings such as group-dilated convolutions [The architectural and technical DL design choices highlighted in this report are the results of heuristic searches of different aspects of the networks that are not presented here. Indeed, this is still often the best exploration strategy due to the immense time and resource requirements for training large DL models. The chosen designs are those we found either to achieve the best performance or to be the simplest. In particular, we experimented with other loss functions . The results are displayed in Fig. The results from the association analysis for the categorical variables  are presented in Fig. p val = 0.021), and p val = 0.040).The relationships between the continuous clinical data  and segmentation performance are displayed in Fig. The statistical comparison between the results of the 70/30 and 50/50 dataset split is presented in Fig.


To define the influence of air, SF6 gas and silicon oil 1000cs tamponade injection and oil tamponade removal on corneal biomechanics, central corneal thickness and intraocular pressure. 77 eyes referred to vitrectomy were divided into 4 groups: 19 to air tamponade, 21 to SF6 tamponade, 19 to oil tamponade, 18 to oil tamponade removal. Pre- and postoperative corneal hysteresis, corneal resistance factor, corneal thickness, Goldman intraocular pressure (GAT) and corneal compensated intraocular pressure (IOPcc) were analysed. GAT and IOPcc did not change after the air or SF6 tamponade. The oil tamponade injection caused increase in GAT and IOPcc, while the oil removal caused reduction in those parameters. In all groups, preoperative and postoperative values of GAT and IOPcc did not differ. There was no change in corneal thickness and biomechanics after air, SF6 or oil tamponade while after removal of oil those parameters are decreased. The air, SF6 and oil tamponade does not change the corneal thickness and corneal biomechanics. The removal of oil causes decrease in corneal thickness and biomechanics which can lead to bias in intraocular pressure measurement. GAT and IOPcc did not differ in eyes pre- and post-vitrectomy, being similarly reliable measure. Pars plana vitrectomy (PPV) is a common surgical procedure currently used for a huge spectrum of vitreal and retinal diseases. The use of intraocular air, gas or silicone oil tamponade increased the efficiency and safety of the PPV surgery.1. Additionally, the postoperative change in central corneal thickness (CCT) can also influence the reliable IOP measurement in these patients. The corneal response technology, currently used in clinical tonometry, enables the assessment of corneal biomechanical properties as: corneal hysteresis (CH) and corneal resistance factor (CRF), potentially improving the accuracy of the IOP measurement.On the other hand, it is well known that the presence of vitreous substitutes carry a risk of some early or late postoperative complications. The former are mostly associated with an increase in intraocular pressure (IOP) and glaucoma due to not fully controlled gas tamponade expansion. Late postoperative complications usually occur in eyes with long-lasting silicone oil tamponade and the chronically elevated IOP as a result of overfilling with silicon oil or a presence of emulsified silicon oil droplets in the anterior chamber. Both mechanisms can lead to blockage of the trabecular meshwork outflow or iridocorneal angle closure. On the other hand, gas or silicon oil tamponade can potentially alter the viscoelasticity and stiffness of the cornea described by corneal biomechanical parameters e.g. corneal hysteresis (CH) and corneal resistance factor (CRF) what in turn is the reason of difficulty in precise and reliable monitoring of IOPThe aim of this retrospective study was to define the influence of air, sulfur hexafluoride (SF6) expanding gas, silicon oil 1000cs tamponade administration and removal on corneal biomechanical parameters, CCT and the IOP measurement.The retrospective research was performed in the Department of Ophthalmology and Vision Rehabilitation of University Clinical Hospital in Lodz-Poland. The analyzed medical data was collected between 2019 and 2021. The inclusion criteria for the study comprised: referral to the PPV surgery for following selected indications: rhegmatogenous retinal detachment (RRD), macular hole as well as planned silicon oil tamponade removal. All the patients enrolled for the study were pseudophakic in the studied eye. The study was approved by the Bioethics Committee at the Medical University of Lodz. All methods were performed in accordance with the guidelines and regulations of the Declaration of Helsinki. All patients enrolled in the study had previously signed a voluntary informed consent to the treatment and use of the results of ocular biometry, biomechanics and IOP measurements in future retrospective studies.The exclusion criteria were as follows: the history of any corneal disease , history of corneal refractive surgery, cataract surgery history less than 3 months, active or history for intraocular inflammation, primary or secondary glaucoma, inadequate lid closure, diabetes mellitus with any ocular complications of diabetes.After the inclusion process, the medical data of patients were divided into four study groups. All patients treated for macular hole underwent 23 gauge (G) PPV followed by intravitreal air or SF6 gas injection. The latter was chosen based on the size of the macular hole, according to the Modified Gass classification. The first group comprised 19 eyes with macular hole (MH) at the stage 1a—(3 cases), 1b—(5 cases) and stage 2—MH ≤ 400 µm (11 cases) which were referred to PPV with intravitreal air injection. The second ōstudy group consisted of 21 eyes with MH at the stage 3 (MH > 400 µm) and stage 4 (MH > 400 µm with a complete posterior vitreous detachment). Those patients (2nd study group) were managed by PPV with the inverted inner limiting membrane (ILM) flap technique with SF6 tamponade. The concentration of SF6 gas that we used was 25% in all treated eyes. The third study group included 19 eyes with rhegmatogenous retinal detachment (RRD) in whom the PPV with 1000cs silicone oil tamponade was performed. The fourth group was composed of 18 eyes planned to silicon oil 1000cs tamponade removal of at least 6-months duration. In 6 patients with silicone oil tamponade lasting more than 9 months, we observed an emulsification of silicone oil. The group 4 consisted of independent cases. None of the patients in group 3 was later included in group 4.Medical data of enrolled patients included the pre-operative slit lamp examination and funduscopy. Swept source OCT was performed 7 days prior the surgery to visualize the macular region and classify patients to the 1st and 2nd study group. The measurement of corneal biomechanical properties, IOP and CCT were taken the day before the surgery and on the 1st and 7th day after the surgery. The ocular response analyzer (ORA) provided results of CH, CRF and corneal compensated intraocular pressure (IOPcc). Additionally, for each measurement, the device estimated the value of the waveform score (WS), which defines the quality of the measurement based on the symmetry of applanation related to the condition of the patient's cornea. WS values were presented on a scale from zero (0) to ten (10). The Goldmann applanation tonometry (GAT) was used for IOP assessment. CCT was measured by Tomey Optical Biometer OA-2000.The distribution of all measurable variables was tested using the Shapiro–Wilk test. All data showed a normal distribution and were presented as mean and standard deviation (SD). The Student’s t-test for dependent samples was used to compare pre- and postoperative values of the same parameter in the same study group. The impact between two variables was assessed by the coefficient of determination, which is the square of the correlation coefficient. The differences between the mean values of parameters were considered as a statistical significant, where the p value was less than 0.05. The statistical analysis was performed using Statistica 12.The study was approved by the Bioethics Committee at the Medical University of Lodz. All patients enrolled in the study had previously signed a voluntary informed consent to the use of their medical data in the retrospective study.In the first study group  there were statistically significant differences in mean values of: CCT  and WS  when compared the pre and postoperative measurement taken on 1st postoperative day  both GAT  and IOPcc  were significantly increased on 1st postoperative day. In turn, all the following parameters were significantly decreased on 1st postoperative morning: CCT , CH , CRF , WS   both GAT and IOPcc were significantly elevated on 1st postoperative day respectively: GAT  and IOPcc (t = − 5.47 p < 0.05). Similarly, GAT  and IOPcc  were significantly elevated on the 7th postoperative day in comparison to the preoperative values. CH values were significantly decreased on 7th postoperative day . In turn, there was a significant rise in postoperative CCT  and WS (t = 4.66 p < 0.05) on 1st postoperative day , GAT , IOPcc  and WS  were significantly reduced on 1st postoperative day. In turn, when observed on the 7th day after the surgical procedure, the values of CCT , GAT , IOPcc , CH  and CRF  were significantly reduced in comparison to the preoperative values Fig. . The val5. In addition, the use of intravitreal substitutes can cause corneal changes such as: loss of corneal endothelial cells, corneal decompensation, band keratopathy and can induce postoperative changes in CCT and corneal biomechanical parameters as CH and CRF11. This changes, in turn, influence the accurate IOP measurement even with the leading tonometry methods.The common use of vitreous substitutes such as: air, gas , silicon oil  tamponade improve the PPV outcomes, however it is also related to some postoperative complications. One of the most severe complications following the PPV surgery with tamponade injection is increase in IOP, which can lead to mechanical compression and blood flow disturbances within the optic nerve head. The damage to retinal nerve fiber layer (RNFL) and ganglion cell complex (GCC) results in irreversible glaucomatous changes followed by visual field defects12. Our results strongly confirmed the Muether's reports, as there was significant rise in GAT and IOPcc on 1st postoperative day in patients with SF6 tamponade when compared to preoperative measurements. In contrast, in the group where the air tamponade was applied, there was no significant difference in GAT or IOPcc when measured on the 1st day after the surgery. In the study of Teke M, who compared IOP values measured with ORA (IOPg and IOPcc) before and 1 week after the PPV with SF6 or C3F8 injection, there was significant rise of IOPcc and IOPg in group with long acting C3F8 gas, with no significant change of IOPcc and IOPg values in SF6 group7. This is consistent with our results, as there was no significant rise in GAT and IOPcc in both groups of patients, who underwent PPV with intravitreal air or SF6 injection when measured on 7th postoperative day. It is thought that the reason for lack of change in IOP in patients with air or SF6 injection 7 days after the surgery is lack of air expansile properties and slightly expansile concentration of SF6 (25%) which does not elevate IOP significantly.In the study of Muether et al., in eyes after PPV with SF6 tamponade, the early significant rise in GAT (measured on the first postoperative morning) was noted in 37.9% of cases14. The mechanism of intraocular elevation is explained by various mechanisms: oil-induced intraocular inflammation, mechanical obstruction of trabecular meshwork by oil droplets, anterior displacement of irido-lenticular diaphragm with consecutive anterior chamber angle narrowing, pupillary block with silicon oil displacement to the anterior chamber, prone head position in the early postoperative period14. Similarly, our results showed a significant rise in GAT and IOPcc values in patients who underwent PPV with silicone oil tamponade, when measured on the 1st and 7th postoperative day in comparison to the preoperative measurement.The recent clinical studies suggest the significant rise in IOP after PPV with silicon oil tamponade15. Similarly, Issa et al. observed decrease in IOP in patients after silicon oil tamponade removal16. Moreover, lots of authors described significant intraocular hypotony, defined as IOP values of 5 mmHg or less after the silicon oil removal20. However, early intraocular hypotony was not observed in our study as the mean values of GAT and IOPcc were both above 9.0 mm Hg  when measured on 1st day postoperatively. In all groups enrolled in our study, the preoperative and postoperative values of GAT and IOPcc did not differ significantly. Moreover, our study showed strong positive correlation between GAT and IOPcc what can suggest that both measurements are similarly reliable in patients who have undergone PPV with tamponade injection or removal.In turn, after the removal of silicone oil tamponade, there was observed a significant lowering in both GAT and IOPcc on 1st and 7th postoperative day when compared to preoperative values. These results are consistent with the previous report of Jawad et al., which revealed significantly lowered values of IOP in eyes after removal of silicon oil in comparison with preoperative valuesOn the 1st postoperative day, corneal biomechanical parameters as CH and CRF were significantly lowered in patients who underwent PPV with SF6 injection. In the rest of study groups following PPV procedures, CH and CRF did not change significantly on the 1st postoperative day. CCT values were significantly increased on 1st postoperative morning in eyes with air tamponade and oil tamponade in contrast to eyes with SF6 tamponade administered, where CCT was significantly decreased. Following oil tamponade removal, the CCT mean value did not change significantly on the 1st postoperative day. Although the results of measurements on the 1st day after surgery suggest differences in the parameters of the corneal biomechanics, the authors believe that this is the effect of poor reliability of the measurements, as evidenced by a significant reduction in the WS coefficient. WS coefficient  was significantly reduced in all groups of patients when measured on the first day after surgery.In contrast, there were no significant changes of WS values on 7th postoperative day, when compared to the preoperative values in all study groups. This observation may suggest that the measurements of IOP and biomechanical properties of the cornea made on the 7th postoperative day were burdened with a smaller measurement error than those made on the 1st day after PPV.22.Our results showed no statistically significant change in corneal parameters including CCT, CH and CRF as assessed on the 7th day after the PPV with air or SF6 tamponade. Similarly, CCT and CRF did not change significantly on the 7th day after silicone oil injection, however the mean value of CH decreased significantly in this group of patients. The lowering of the CH, in turn, can be related to a significant rise in IOPcc and GAT, observed in these patients on 7th postoperative morning. This relationship proved by our study in eyes with silicon oil tamponade was also previously described in some clinical studies over the effect of rise in IOP on CH in glaucomatous eyes23. The pathomechanism of CCT changes in patients with elevated IOP is not entirely clear24. It is suggested that CCT reduction in glaucomatous eyes may be the effect of remodeling of the corneal stroma under the increased IOP and pressure exerted on stromal collagen fibers25. This, in turn, leads to the stretching of the cornea and in consequence its thinning25. As it was mentioned above, remodeling of the corneal stroma in response to chronically elevated IOP in case of on silicon oil tamponade can be responsible for the decreased values of CH. According to our results, CRF values were elevated in eyes with silicon oil and have been significantly reduced after its removal. It is suggested that elevated CRF is a derivative of increased corneal stiffness through a transient tension in the stromal collagen fibers in response to an increase in IOP26. As we suppose, after the removal of silicone oil and reported significant decrease in IOPcc and GAT, the taut stromal fibers of the cornea relaxed leading to postoperative reduction of CRF.In patients who underwent PPV surgery with removal of silicone oil tamponade, the CCT, CH and CRF values were significantly reduced when measured on 7th day postoperatively in comparison to preoperative values. The question arises, whether the reduction was related to the surgery itself or it was a result of 6-months presence of intraocular silicon oil tamponade or both. Current studies suggest that in eyes with chronically elevated IOP, CCT is significantly reduced, what can be consistent with long-term increase in IOP related to silicon oil tamponade lasting 6-months in our patientsOur results revealed a strong positive correlation between CH and CRF in all study groups pre and postoperatively, except patients who underwent silicon oil removal. In the other words, the corneal viscoelasticity described by CH correlated with stiffness and total resistance of the cornea described by CRF in patients referred to PPV, as well as in eyes that have undergone PPV with air, SF6 or silicon oil injection. This relationship between CH and CRF was not observed in patients prior, as well as on 7th day after silicon oil tamponade removal. What is more, there was a strong preoperative and postoperative correlation of CCT only with CRF but not with CH. This result is a strong argument that the silicon oil tamponade affects corneal viscoelasticity (CH), its stiffness (CRF) and thickness (CCT) in different ways, and this effect persists even after the removal of tamponade.The major limitation of our study is the retrospective design. Further prospective clinical trials with longer follow up time and larger sample size are needed to expand the results of the study. Moreover, the duration time of silicone oil tamponade in the fourth study group was at least 6 months, which could have caused structural changes in the studied eyes. The morphological changes could have had an influence on the IOP, corneal biomechanics and CCT values measured, causing bias in the obtained results.The PPV with intravitreal air or 25% SF6 injection does not significantly influence GAT and IOPcc, while PPV with silicone oil tamponade procedure causes significant rise in GAT and IOPcc values when measured on the 7th postoperative day. In turn, the removal of silicone oil tamponade causes a significant drop in both GAT and IOPcc. GAT and IOPcc did not differ significantly but correlated positively in pre-and postoperative assessments, which can prove that both measures are similarly reliable in patients after PPV procedure. The PPV with intravitreal air or 25% SF6 injection does not influence CCT and its biomechanical parameters including CH and CRF, when assessed on the 7th postoperative day. Similarly, PPV with silicone oil tamponade does not change the values of CCT and CRF, but it causes a significant decrease in CH. In turn, the PPV with removal of silicone oil tamponade causes significant decrease in CCT, CH and CRF values. Finally, there is a strong positive correlation between CH and CRF in patients who undergone PPV with air, SF6 or silicon oil injection. However, this relationship between CH and CRF was not observed in patients just after the removal of silicon oil tamponade. This result suggests that the chronic oil tamponade decreases corneal viscoelasticity (CH) and its stiffness (CRF) indeed but in a different way as evidenced by the lack of correlation between these parameters. The solution to this problem remains unclear and will become the subject of further research on the impact of intraocular surgical procedures on corneal biomechanics and precise IOP measurement.


In recent years, due to the advent of several additives and innovations, asphalt mix design has become more complex. The mixes meeting the volumetric mix design requirements may still fail prematurely in the field. Thus, a transition from a simplistic volumetric-based mix design to a performance-based mix design is required, which was also envisioned in the Strategic Highway Research Program (SHRP) and Superpave mix design. In addition to performance verification, asphalt mix designs should also be evaluated for the life-cycle costs and environmental impact to encourage durable as well as sustainable and cost-effective alternatives. In this study, three asphalt mixtures with different reclaimed asphalt pavement (RAP) contents and additives were evaluated for cracking and rutting performance by using different performance thresholds for asphalt mixtures that are generally used in the construction of high-volume roads in Oregon. A balanced mix design process was followed to determine the required binder content for the three mixtures. Based on the life cycle cost and environmental impact analyses, the mixture with warm mix additive (WMA) was selected as the most economically and environmentally viable asphalt mixture to be used for construction in Oregon. Asphalt mix design, at its core, is a simple process of blending aggregates (of different sizes), asphalt binder, and, in most cases now, recycled asphalt materials (RAMs). The mix obtained from this design process is expected to meet the specifications established by the state departments of transportation (DOTs) or government agencies. In recent years, due to the advent of several additives and innovations, asphalt mix design has become more complex. Moreover, the mixes, despite meeting the volumetric requirements, may sometimes fail prematurely in the field. For example, the volumetric design results in a lower binder content in asphalt mixes in Oregon, making them susceptible to cracking prematurely. Oregon Department of Transportation’s (ODOT’s) Pavement Management System has shown that asphalt mixes placed in the last 20 years have had a tendency to develop premature cracking after 6 to 8 years of service before reaching the structural design life of 15 years. This happens because the specifications were established based on the assumption that there is a strong empirical correlation between the volumetric parameters of mix design, such as air void content, voids in mineral aggregates (VMAs), voids filled with asphalt (VFAs), and the field performance of asphalt pavements. However, this correlation is generally valid when there are no special additives or technologies used for the production of asphalt. As such, the use of new additives and strategies, such as Reclaimed Asphalt Pavement (RAP), warm mix asphalt (WMA), and polymer-modified asphalt binders (PMA), in the asphalt mix design may complicate the asphalt mix design process and result in unreliable field performance prediction. Thus, a transition from a simplistic volumetric-based mix design to a performance-based mix design is required, which was also envisioned in the Strategic Highway Research Program (SHRP) and the Superpave mix design ,10,11.In addition to performance verification, asphalt mix designs should also be evaluated for the life-cycle cost and environmental impact to encourage durable as well as sustainable alternatives. Among several strategies, three promising strategies that have gained the attention of researchers and agencies alike are the (1) increased in-place density, (2) high percentage of RAP in the mix, and (3) use of WMA. Increased in-place pavement density or reduced air voids have been found to significantly improve both the cracking and rutting performance of asphalt pavements ,14,15,16Similar to high-density and high-RAP mixes, the use of WMA in asphalt pavements has numerous advantages over conventional HMA mixes. According to ,22, WMA Design three asphalt mixtures with different constituents to determine the most effective strategies for road construction;Evaluate the trial mixes for cracking and rutting performances;Determine the design binder content range for each mix using a balanced asphalt mix design method developed for Oregon by incorporating performance tests for rutting and cracking into the current volumetric design process ;Determine the cost and environmental impact of all three mixtures by performing life cycle cost and environmental impact analysis;Recommend the “best” asphalt mixture for the given conditions by considering the cost-effectiveness, environmental impact, and long-term performance of the mixes.In Oregon, fatigue cracking is the major distress mode for asphalt concrete pavement structures. It is one of the main reasons for large road maintenance and rehabilitation expenditures, as well as reduced user comfort and increased fuel consumption due to high road roughness. Increasing the asphalt binder content, using modified binders, and/or using softer binder grades were proved to improve fatigue cracking resistance ,25. ColeThis section provides information about the materials used in this study . The materials were sampled from an asphalt plant located near Tigard, Oregon. The samples were produced at Oregon State University Asphalt Materials and Pavements (OSU-AMaP) Laboratory from the raw materials that were used in the actual field construction project.Three different asphalt mixtures were used in this study . These mixes varied in gradation, amount of RAP content, and presence of additives. Mix1 was further divided into two mixes, Mix1_AV5 and Mix1_AV7, differing by the compacted air void contents of the test samples  to quantify the impact of density on performance (both designed by the Superpave mix design method via Superpave Gyratory Compactor (SGC)). Mix2 had 45% RAP content, and Mix3 was identical to Mix1 except that in Mix3, a chemical warm-mix additive was used. Both Mix2 and Mix3 were compacted to 93-percent of the theoretical maximum density (±0.5%) in a gyratory compactor to produce test samples with a conventional 7% air-void content. In this study, BMD samples were produced with 7% air-void content as a 93% density during construction is the expected average density for contractors in Oregon.mm) samples were mixed in triplicate according to AASHTO T 312-12 [mm values were determined as per AASHTO T 209-12 procedures. Subsequently, three replicate mix design samples were prepared for each binder content and compacted in the gyratory compactor.T 312-12  and theiThe binder content corresponding to the target design air void was selected as the optimum binder content (OBC) for each mix. The volumetrics and the other mix design variables of the three mixes used in this study are summarized in It should be noted that RAP and hydrated lime are added by weight of the mix, whereas WMA is added by weight of the asphalt binder. Hydrated lime is an inorganic compound, whereas the chemical WMA has polymeric content. Dust-to-binder ratios for all asphalt mixtures are within the limits required by the ODOT (0.8–1.6).The AASHTO T 312-12  specificFor warm mix asphalt sample preparation, aggregates and RAP were batched following the same guidelines as the hot mix asphalt. Before mixing, the binder and the chemical warm mix additive were mixed using a stationary countertop mixer. The chemical additive dosage was calculated according to Equation (1) , consideTo evaluate the cracking and rutting performance of the asphalt mixture samples prepared in the study, SCB and Hamburg Wheel Tracking Test (HWTT) methods were followed, respectively. The AASHTO T 324  standardIn a previous research study performed at Oregon State University, the semi-circular bend (SCB) test was selected as the most effective cracking experiment to characterize asphalt mixtures used in Oregon . TherefoThe fatigue cracking test (SCB) was conducted at an intermediate temperature (25 °C) when the mix was neither too soft nor too brittle. The rutting test (HWTT) was investigated at a hotter temperature of 50 °C at which the mix was soft.mm measurement and volumetric design stages.This study was performed to evaluate three different mixes for their cracking and rutting performance and volumetrics. The general experimental plan followed in this study is given in design from volumetric mix design, ACdesign – 0.5%, and ACdesign + 0.5%. For Mix2, ACdesign – 0.5% was too low and could result in a very dry mix (due to high RAP content) and, hence, the three asphalt contents considered were: ACdesign from volumetric mix design, ACdesign + 0.5%, and ACdesign + 1%.Test specimens were prepared from the three selected mixes see  with thethreshold = 8) mixtures as determined by Coleri et al. [The comparison of the cracking (SCB) performance of all asphalt mixtures along with their BMD variants is presented in i et al. . It can be observed from From threshold = 2.5 mm for Level 4 mixes determined by Coleri et al. [i et al. ). In thiA higher binder content expectedly resulted in a higher rut depth for all the cases, as observed in Mix3 showed the highest rut-depth among all three mixes as the warm mix additive and reduced aging during the mix preparation due to the lower mixing temperature, making the mix softer. The high-RAP mix showed a higher rut depth than the low-RAP mix (Mix1), but it should be noted that the high-RAP mix also had a higher binder content (0.2% more binder for every case).The average rut depths of all the mixtures were lower than 3.0 mm (except the WMA mix with the highest binder content), which is significantly lower than the maximum rut depth allowed by several agencies in the U.S. (which is 12.5 mm). This result suggested that all mixes used in this part of the study are on the “dry” side and need more asphalt binder to improve their cracking resistance while still meeting the 12.5 mm maximum rut depth requirement.The balanced mix design concept is utilized to determine the maximum and the minimum asphalt content that “balances” the rutting and cracking performance of the asphalt mixtures. In Oregon, Coleri et al.  recommenFrom RAP: USD 20/ton;Aggregate: USD 13/ton;PG70-22ER binder: USD 490/ton;WMA additive: USD 70/ton (added to the per ton cost of the binder for a 0.7% WMA by weight of binder).In this study, a cost calculation tool created by Coleri et al.  was usedAn additional step of calculating the production burner cost, which was not included in the original cost calculation tool, was incorporated in this study. The burner fuel cost can be the key factor in determining whether the HMA or the WMA is the most cost-efficient asphalt mixture. In order to assess the contribution of the production costs, a fuel consumption of 2 gallons of diesel fuel per ton for HMA and 1 gallon (3.78 L) of diesel fuel per ton for WMA with chemical additive  were conLane—single;Width—3.7 m;Length—1.61 km.As a first step, life-cycle cost analyses were carried out on different mixes used in this study to compare the impact of the basic constituents of the HMA mix on life-cycle costs. Then, a second set of LCCA was performed after including the plant burner costs to be able to determine the cost impact of using warm-mix. The material costs were based on the cost calculation tool described earlier. The following assumptions were made for the pavement geometry:t = estimated agency costs at year t;Cr = interest rate;T = number of time periods.A 4-percent interest rate was used to arrive at the Net present value (NPV) of agency costs for a 60-year analysis period by using Equation (2):As all mix designs had a 20-year design period, it was assumed that the same mixtures would be used every 20 years for the next 60 years. In this study, the NPV was calculated for all the mixes, and the equation below describes how the NPV for Mix1_AV5 was calculated (as an example).In It can be observed from Pavement Life Cycle Assessment (LCA) procedures were used to calculate the environmental impact of each pavement mixture for the material production and construction stages of the pavement life cycle. In this study, a “cradle to gate” approach with the software’s pre-defined system boundaries was adopted. The functional unit was input as roadway parameters. The software utilizes proprietary databases on materials, energy, and transportation and reports a detailed set of life cycle impact assessment results. Moreover, the software supports the United States Environmental Protection Agency (US EPA) Tool for Reduction and Assessment of Chemical and Other Environmental Impacts (TRACI)-based midpoint life cycle impact assessment (LCIA) indicators: global warming potential, acidification potential, eutrophication potential, human health particulate, smog potential, ozone depletion potential, total primary energy consumption, non-renewable energy consumption, and fossil fuel consumption. Three of these indicators were selected for the LCA study as discussed later in the paper. For a base case, a mixture of 6% binder content and 20% RAP content was selected (referred to as Mix F in the plots). This represents the most common pavement design in Oregon. The roadway geometry for all cases was defined to have three lifts of pavement with thicknesses of 63.5 mm, 139.7 mm, and 139.7 mm. The length of the roadway was set to 1 km, with three lanes of 3.7 m each, a typical width for roadways in the U.S.In order to determine the differences in environmental performance, the primary characteristics for each pavement design were entered into the Pavement LCA software. Materials by the percentage of total mixture weight were input  along with the asphalt type (HMA or WMA). All factors for which no data were available or factors that were not considered (such as hauling distance) were set to be default and equal between mixes to not affect the results. Pavement–vehicle interaction (PVI), which is a separate option in the software, was excluded entirely as all mixes were designed for 20 years, and PVI-related vehicle operating costs should be theoretically equal for all analyzed mixtures.In order to accurately compare different pavement designs, each mixture was assumed to conform to a 60-year lifespan, with rehabilitation occurring every 20th year. For rehabilitation, 50.8 mm of asphalt is milled and removed and then replaced .Results were exported from the software and plotted. Results are given in Global Warming Potential (GWP), Acidification Potential (AP), and Eutrophication Potential (EP) for all three mixtures of this study. Mix1 with the 5% air void case was not evaluated, as density does not directly change the environmental impact during the material production and construction stages. Units do not represent the chemical composition of the pollution itself but instead represent the amount of a standard normalizing factor representative of each pollution type .Mixtures 1, 2, and 3 each performed nearly equivalently, with Mix1 exhibiting slightly worse performance and Mix3 (warm-mix) being the best. All mixtures had a significantly lower impact when compared to the typical Oregon asphalt mixture with lower RAP content. This is likely caused by the difference in the production process between HMA and WMA and higher RAP content in the designed mixtures. This result also proves the importance of further increasing the RAP content of asphalt mixtures for the environment. The results for acidification potential were similar to those of global warming potential. Mix F again performed poorly, while Mixes 1, 2, and 3 performed similarly. Mix3 (warm-mix) again outperformed both Mixes 1 and 2. This is most likely a result of the WMA production process being significantly less energy-intensive, as well as the design allowing for a lower binder content and higher RAP.The results indicate that mixtures 1, 2, and 3 again outperformed the typical pavement design. Mix3 (warm-mix) performed the highest of the three design mixtures. The differences between the three design mixtures and the typical mixture are likely explained by the increased RAP content in the three designs, as well as the lower energy cost of WMA. Differences between the three designs are likely to be caused by the slight difference in binder content and RAP content.Mix3 has a cracking resistance significantly higher than all other mixtures. A higher cracking resistance for Mix3 is likely to be a result of the use of a warm mix additive. It is important to mention that the mixtures with warm mix additive show a better cracking resistance than other corresponding mixes with the same or higher binder contents.The cracking resistance for Mix1 with 5% air-void is slightly higher than the same mix with 7% air void. Thus, the density of the mix appears to have a significant effect on the cracking resistance.The high-RAP mix (Mix2) has a better cracking resistance than the low-RAP mix (Mix1) according to SCB test results, but this is expected to be a result of the higher binder content of Mix2 specimens. The higher BMD binder content of Mix2 (when compared to lower-RAP mix—Mix1) suggests that the performance of a high-RAP mixture can be improved by slightly increasing the binder content.Although Mix2 (45% RAP) has a higher BMD binder content than Mix1 (30% RAP), it is still more cost-effective due to the increased use of recycled asphalt material in the mix.Mix1_AV5 has the best rutting resistance among all the mixes. Samples for only this mixture are compacted at 5% air-void. A higher density (2% higher than 7% air-void samples) results in an improved rutting resistance. It is important to note that a 2% increase in density results in significant improvements in both rutting and cracking performance. Although not simulated in this study, increased density is also expected to reduce the long-term aging and moisture susceptibility of the asphalt mixtures due to reduced permeability.The mix with 45% RAP content (Mix2) has the lowest NPV over the course of the 60-year analysis period, followed by the warm mix asphalt (Mix3) and then the mix with 30% RAP (Mix1) when only the raw material costs are considered. However, this ranking is altered when the plant burner fuel consumption is incorporated into the life cycle cost analysis. When the burner costs are included in the LCCA, the most cost-effective mix is the warm mix asphalt (Mix3), considering the reduced production (burner) temperature and, consequently, less fuel consumption during production.Mix3 (warm-mix) is also the most environmentally friendly mix with lower expected GWP, EP, and AP values for a 60-year analysis period.In this study, volumetric and balanced mix designs were conducted to determine the optimum asphalt binder content for three different asphalt mixtures. Cost-effectiveness and the environmental impact of those asphalt mixtures were also quantified and compared. Based on the quantified cost, performance, and environmental impact values, the mixture with warm-mix additives (Mix3) was selected as the most preferable asphalt mixture with the highest cracking resistance, lowest cost, and lowest environmental impact. Other conclusions derived from this study are as follows: