Lisha Yao, Yingda Xia, Zhihong Chen, Suyun Li, Jiawen Yao, Dakai Jin, Yanting Liang, Jiatai Lin, Bingchao Zhao, Chu Han, Le Lu, Ling Zhang, Zaiyi Liu, Xin Chen
Automated colorectal cancer (CRC) segmentation in medical imaging is the key to achieving automation of CRC detection, staging, and treatment response monitoring. Compared with magnetic resonance imaging (MRI) and computed tomography colonography (CTC), conventional computed tomography (CT) has enormous potential because of its broad implementation, superiority for the hollow viscera (colon), and convenience without needing bowel preparation. However, the segmentation of CRC in conventional CT is more challenging due to the difficulties presenting with the unprepared bowel, such as distinguishing the colorectum from other structures with similar appearance and distinguishing the CRC from the contents of the colorectum...
April 30, 2024: IEEE Transactions on Neural Networks and Learning Systems