Colorectal Tumor Segmentation of CT Scans Based on a Convolutional Neural Network With an Attention Mechanism

• Published in: IEEE access Vol. 8; pp. 64131 - 64138
• Main Authors: Pei, Yun, Mu, Lin, Fu, Yu, He, Kan, Li, Hong, Guo, Shuxu, Liu, Xiaoming, Li, Mingyang, Zhang, Huimao, Li, Xueyan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; attention mechanism; Cancer; Coders; Colorectal tumor; Computed tomography; Convolution; Decoding; deep learning; Feature extraction; Feature maps; Image segmentation; Medical imaging; Modules; Neural networks; Tumors
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2982543

Due to the irregularity of colorectal tumor contours, it is a challenging task for clinicians to segment colorectal tumors manually in CT scans. To solve this problem, a novel algorithm based on a convolutional neural network with an attention mechanism is proposed to automatically achieve tumor segmentation. The proposed network consists of three major modules: an encoder module, which is fed CT scans to attain the feature map; a dual attention module, which includes a channel attention module and a position attention module to obtain more contextual information in the deep layer of the network; and a decoder module, which restores the feature map to the original size of the input images. We used 1131 CT slices of colorectal tumors to train and test the proposed network. Compared with U-Net and CE-Net, the Dice coefficient increased by 1.46% and 0.66% respectively, for our model. The comprehensive results show that the proposed network performs more effectively in colorectal tumor segmentation than the other methods.