Fully Convolutional Network-based Multi-Task Learning for Rectum and Rectal Cancer Segmentation
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In this study, we present a fully automatic method to segment both rectum and rectal 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.
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