Deep learning for segmentation of 49 selected bones in CT scans: First step in automated PET/CT-based 3D quantification of skeletal metastases.
PURPOSE
The aim of this study was to develop a deep learning-based method for segmentation of bones in CT scans and test its accuracy compared to manual delineation, as a first step in the creation of an automated PET/CT-based method for quantifying skeletal tumour burden.


METHODS
Convolutional neural networks (CNNs) were trained to segment 49 bones using manual segmentations from 100 CT scans. After training, the CNN-based segmentation method was tested on 46 patients with prostate cancer, who had undergone 18F-choline-PET/CT and 18F-NaF PET/CT less than three weeks apart. Bone volumes were calculated from the segmentations. The network's performance was compared with manual segmentations of five bones made by an experienced physician. Accuracy of the spatial overlap between automated CNN-based and manual segmentations of these five bones was assessed using the Sørensen-Dice index (SDI). Reproducibility was evaluated applying the Bland-Altman method.


RESULTS
The median (SD) volumes of the five selected bones were by CNN and manual segmentation: Th7 41 (3.8) and 36 (5.1), L3 76 (13) and 75 (9.2), sacrum 284 (40) and 283 (26), 7th rib 33 (3.9) and 31 (4.8), sternum 80 (11) and 72 (9.2), respectively. Median SDIs were 0.86 (Th7), 0.85 (L3), 0.88 (sacrum), 0.84 (7th rib) and 0.83 (sternum). The intraobserver volume difference was less with CNN-based than manual approach: Th7 2% and 14%, L3 7% and 8%, sacrum 1% and 3%, 7th rib 1% and 6%, sternum 3% and 5%, respectively. The average volume difference measured as ratio volume difference/mean volume between the two CNN-based segmentations was 5-6% for the vertebral column and ribs and ≤3% for other bones.


CONCLUSION
The new deep learning-based method for automated segmentation of bones in CT scans provided highly accurate bone volumes in a fast and automated way and, thus, appears to be a valuable first step in the development of a clinical useful processing procedure providing reliable skeletal segmentation as a key part of quantification of skeletal metastases.
['Lindgren Belal|Sarah|S|', 'Sadik|May|M|', 'Kaboteh|Reza|R|', 'Enqvist|Olof|O|', 'Ulén|Johannes|J|', 'Poulsen|Mads H|MH|', 'Simonsen|Jane|J|', 'Høilund-Carlsen|Poul F|PF|', 'Edenbrandt|Lars|L|', 'Trägårdh|Elin|E|']
D000328:Adult D000368:Aged D000369:Aged, 80 and over D059925:Anatomic Landmarks D001859:Bone Neoplasms D000077321:Deep Learning D005260:Female D006801:Humans D008297:Male D008875:Middle Aged D016571:Neural Networks, Computer D015588:Observer Variation D000072078:Positron Emission Tomography Computed Tomography D064129:Prostatic Neoplasms, Castration-Resistant D015203:Reproducibility of Results D014057:Tomography, X-Ray Computed D047368:Tumor Burden
Bone Metastases Prostate cancer PET/CT Artificial intelligence