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In post-operative radiotherapy for prostate cancer, the cancerous prostategland has been surgically removed, so the clinical target volume (CTV) to beirradiated encompasses the microscopic spread of tumor cells, which cannot bevisualized in typical clinical images such as computed tomography or magneticresonance imaging. In current clinical practice, physicians segment CTVsmanually based on their relationship with nearby organs and other clinicalinformation, per clinical guidelines. Automating post-operative prostate CTVsegmentation with traditional image segmentation methods has been a majorchallenge. Here, we propose a deep learning model to overcome this problem bysegmenting nearby organs first, then using their relationship with the CTV toassist CTV segmentation. The model proposed is trained using labels clinicallyapproved and used for patient treatment, which are subject to relatively largeinter-physician variations due to the absence of a visual ground truth. Themodel achieves an average Dice similarity coefficient (DSC) of 0.87 on aholdout dataset of 50 patients, much better than established methods, such asatlas-based methods (DSC<0.7). The uncertainties associated with automaticallysegmented CTV contours are also estimated to help physicians inspect and revisethe contours, especially in areas with large inter-physician variations. Wealso use a 4-point grading system to show that the clinical quality of theautomatically segmented CTV contours is equal to that of approved clinicalcontours manually drawn by physicians.

A deep learning-based framework for segmenting invisible clinical target  volumes with estimated uncertainties for post-operative prostate cancer  radiotherapy