Fully Convolutional Network-based Multi-Task Learning for Rectum and Rectal Cancer Segmentation
Year: 2019
Type: preprint
Abstract: In this study, we present a fully automatic method to segment both rectum andrectal cancer based on Deep Neural Networks (DNNs) with axial T2-weighted
Magnetic Resonance images. Clinically, the relative location between rectum and
rectal cancer plays an important role in cancer treatment planning. Such a need
motivates us to propose a fully convolutional architecture for Multi-Task
Learning (MTL) to segment both rectum and rectal cancer. Moreover, we propose a
bias-variance decomposition-based method which can visualize and assess
regional robustness of the segmentation model. In addition, we also suggest a
novel augmentation method which can improve the segmentation performance as
well as reduce the training time. Overall, our proposed method is not only
computationally efficient due to its fully convolutional nature but also
outperforms the current state-of-the-art for rectal cancer segmentation. It
also scores high accuracy in rectum segmentation without any prior study
reported. Moreover, we conclude that supplementing rectum information benefits
the rectal cancer segmentation model, especially in model variance.
Source: arXiv (Cornell University)
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3211759145