  In preoperative imaging, the demarcation of rectal cancer with magnetic
resonance images provides an important basis for cancer staging and treatment
planning. Recently, deep learning has greatly improved the state-of-the-art
method in automatic segmentation. However, limitations in data availability in
the medical field can cause large variance and consequent overfitting to
medical image segmentation networks. In this study, we propose methods to
reduce the model variance of a rectal cancer segmentation network by adding a
rectum segmentation task and performing data augmentation; the geometric
correlation between the rectum and rectal cancer motivated the former approach.
Moreover, we propose a method to perform a bias-variance analysis within an
arbitrary region-of-interest (ROI) of a segmentation network, which we applied
to assess the efficacy of our approaches in reducing model variance. As a
result, adding a rectum segmentation task reduced the model variance of the
rectal cancer segmentation network within tumor regions by a factor of 0.90;
data augmentation further reduced the variance by a factor of 0.89. These
approaches also reduced the training duration by a factor of 0.96 and a further
factor of 0.78, respectively. Our approaches will improve the quality of rectal
cancer staging by increasing the accuracy of its automatic demarcation and by
providing rectum boundary information since rectal cancer staging requires the
demarcation of both rectum and rectal cancer. Besides such clinical benefits,
our method also enables segmentation networks to be assessed with bias-variance
analysis within an arbitrary ROI, such as a cancerous region.
