A Novel U-Like Network For The Segmentation Of Thoracic Organs - Shi, Jun; Wen, Ke; Hao, Xiaoyu; Xue, Xudong; An, Hong; Zhang, Hongyan (2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops), 2020)
Accurate segmentation of organs at risk (OARs) in computed tomography (CT) is an essential step in radiation therapy for thoracic cancer treatment. However, the manual segmentation of OARs is time-consuming and subject to inter-observer variation. In this paper, we propose a novel U-like deep convolutional neural network (CNN) architecture, which adopts the encoder-decoder design, to automatically segment the OARs in thoracic CT images. In our method, hybrid dilated convolution (HDC) is employed to enlarge the receptive field of the encoder part, and a pyramid backbone with lateral connections between encoder and decoder is utilized to capture contextual information at multiple scales. To reduce the false-positive segmentation results, we use the multi-task learning strategy to add an auxiliary classifier branch to the network. The experiments demonstrate that the proposed method outperforms other state-of-the-art models and the results have a good consistency with that of experienced radiologists.