RDCTrans U-Net: A Hybrid Variable Architecture for Liver CT Image Segmentation

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 7; p. 2452
• Main Authors: Li, Lingyun, Ma, Hongbing
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 23.03.2022; MDPI
• Subjects: Computed tomography; CT imaging; Deep learning; Diagnostic imaging; dilated convolution; Humans; Image processing; Image Processing, Computer-Assisted - methods; Liver; Liver cancer; Liver Neoplasms - diagnostic imaging; liver tumor segmentation; Medical imaging; Medical imaging equipment; Medical research; Neural Networks, Computer; ResNeXt50; Telecommunication systems; Tomography, X-Ray Computed; transformer; Tumors; U-Net; liver tumor segmentation; transformer; dilated convolution; U-Net; ResNeXt50
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22072452

Segmenting medical images is a necessary prerequisite for disease diagnosis and treatment planning. Among various medical image segmentation tasks, U-Net-based variants have been widely used in liver tumor segmentation tasks. In view of the highly variable shape and size of tumors, in order to improve the accuracy of segmentation, this paper proposes a U-Net-based hybrid variable structure-RDCTrans U-Net for liver tumor segmentation in computed tomography (CT) examinations. We design a backbone network dominated by ResNeXt50 and supplemented by dilated convolution to increase the network depth, expand the perceptual field, and improve the efficiency of feature extraction without increasing the parameters. At the same time, Transformer is introduced in down-sampling to increase the network's overall perception and global understanding of the image and to improve the accuracy of liver tumor segmentation. The method proposed in this paper tests the segmentation performance of liver tumors on the LiTS (Liver Tumor Segmentation) dataset. It obtained 89.22% mIoU and 98.91% Acc, for liver and tumor segmentation. The proposed model also achieved 93.38% Dice and 89.87% Dice, respectively. Compared with the original U-Net and the U-Net model that introduces dense connection, attention mechanism, and Transformer, respectively, the method proposed in this paper achieves SOTA (state of art) results.