  Prostate cancer (PCa) is the most common cancer in men in the United States.
Multiparametic magnetic resonance imaging (mp-MRI) has been explored by many
researchers to targeted prostate biopsies and radiation therapy. However,
assessment on mp-MRI can be subjective, development of computer-aided diagnosis
systems to automatically delineate the prostate gland and the intraprostratic
lesions (ILs) becomes important to facilitate with radiologists in clinical
practice. In this paper, we first study the implementation of the Mask-RCNN
model to segment the prostate and ILs. We trained and evaluated models on 120
patients from two different cohorts of patients. We also used 2D U-Net and 3D
U-Net as benchmarks to segment the prostate and compared the model's
performance. The contour variability of ILs using the algorithm was also
benchmarked against the interobserver variability between two different
radiation oncologists on 19 patients. Our results indicate that the Mask-RCNN
model is able to reach state-of-art performance in the prostate segmentation
and outperforms several competitive baselines in ILs segmentation.
