Automated Computed Tomography-Ultrasound Cross-Modality 3-D Contouring Algorithm for Prostate.
A novel fully automated algorithm is introduced for 3-D cross-modality image segmentation of the prostate, based on the simultaneous use of co-registered computed tomography (CT) and 3-D ultrasound (US) images. By use of a Gabor feature detector, the algorithm can outline in three dimensions and in cross-modality the prostate, and it can be trained and optimized on specific patient populations. We applied it to 16 prostate cancer patients and evaluated the conformity between the automatically segmented prostate contours and the contours manually outlined by an experienced physician, on the CT-US fusion, using the mean distance to conformity (MDC) index. When only the CT scans were used, the average MDC value was 4.5 ± 1.7 mm (maximum value = 9.0 mm). When the US scans also were considered, the mean ± standard deviation was reduced to 3.9 ± 0.7 mm (maximum value = 5.5 mm). The cross-modality approach acted on all the largest distance values, reducing them to acceptable discrepancies.
['Ermacora|Denis|D|', 'Pesente|Silvia|S|', 'Pascoli|Francesco|F|', 'Raducci|Sebastian|S|', 'Mauro|Rudy|R|', 'Rumeileh|Imad Abu|IA|', 'Verhaegen|Frank|F|', 'Fontanarosa|Davide|D|']
D000368:Aged D006801:Humans D007089:Image Enhancement D007090:Image Interpretation, Computer-Assisted D021621:Imaging, Three-Dimensional D007558:Italy D000069550:Machine Learning D008297:Male D008875:Middle Aged D064847:Multimodal Imaging D009426:Netherlands D015588:Observer Variation D010363:Pattern Recognition, Automated D011471:Prostatic Neoplasms D015203:Reproducibility of Results D012680:Sensitivity and Specificity D013382:Subtraction Technique D014463:Ultrasonography
Cross-modality Automated image segmentation Computed tomography imaging Image-guided radiation therapy Radiotherapy Ultrasound imaging