  In post-operative radiotherapy for prostate cancer, the cancerous prostate
gland has been surgically removed, so the clinical target volume (CTV) to be
irradiated encompasses the microscopic spread of tumor cells, which cannot be
visualized in typical clinical images such as computed tomography or magnetic
resonance imaging. In current clinical practice, physicians segment CTVs
manually based on their relationship with nearby organs and other clinical
information, per clinical guidelines. Automating post-operative prostate CTV
segmentation with traditional image segmentation methods has been a major
challenge. Here, we propose a deep learning model to overcome this problem by
segmenting nearby organs first, then using their relationship with the CTV to
assist CTV segmentation. The model proposed is trained using labels clinically
approved and used for patient treatment, which are subject to relatively large
inter-physician variations due to the absence of a visual ground truth. The
model achieves an average Dice similarity coefficient (DSC) of 0.87 on a
holdout dataset of 50 patients, much better than established methods, such as
atlas-based methods (DSC<0.7). The uncertainties associated with automatically
segmented CTV contours are also estimated to help physicians inspect and revise
the contours, especially in areas with large inter-physician variations. We
also use a 4-point grading system to show that the clinical quality of the
automatically segmented CTV contours is equal to that of approved clinical
contours manually drawn by physicians.
