文献类型：期刊文献

英文题名：MC-DC: An MLP-CNN Based Dual-path Complementary Network for Medical Image Segmentation

作者：Jiang, Xiaoben[1];Zhu, Yu[1];Liu, Yatong[1];Wang, Nan[1];Yi, Lei[2]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Technol, Shanghai 200237, Peoples R China;[2]Shanghai Jiao Tong Univ, Sch Med, Ruijin Hosp, Dept Burn, Shanghai 200025, Peoples R China

年份：2023

卷号：242

外文期刊名：COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE

收录：;EI(收录号：20234114846188);WOS:【SCI-EXPANDED(收录号:WOS:001096394700001)】；

基金：The authors greatly appreciate the financial supports of General Program National Natural Science Foundation of China (81971832).

语种：英文

外文关键词：MLP-CNN based dual-path complementary network; dual-path complementarymodule; cross-scale global feature fusion module; cross-scale local feature fusion module; efficient mask feature fusion module; medical image segmentation

摘要：Background: Fusing the CNN and Transformer in the encoder has recently achieved outstanding performance in medical image segmentation. However, two obvious limitations require addressing: (1) The utilization of Transformer leads to heavy parameters, and its intricate structure demands ample data and resources for training, and (2) most previous research had predominantly focused on enhancing the performance of the feature encoder, with little emphasis placed on the design of the feature decoder.Methods: To this end, we propose a novel MLP-CNN based dual-path complementary (MC-DC) network for medical image segmentation, which replaces the complex Transformer with a cost-effective Multi-Layer Perceptron (MLP). Specifically, a dual-path complementary (DPC) module is designed to effectively fuse multi-level features from MLP and CNN. To respectively reconstruct global and local information, the dual-path decoder is proposed which is mainly composed of cross-scale global feature fusion (CS-GF) module and cross-scale local feature fusion (CS-LF) module. Moreover, we leverage a simple and efficient segmentation mask feature fusion (SMFF) module to merge the segmentation outcomes generated by the dual-path decoder.Results: Comprehensive experiments were performed on three typical medical image segmentation tasks. For skin lesions segmentation, our MC-DC network achieved 91.69% Dice and 9.52mm ASSD on the ISIC2018 dataset. In addition, the 91.6% Dice and 94.4% Dice were respectively obtained on the Kvasir-SEG dataset and CVC-ClinicDB dataset for polyp segmentation. Moreover, we also conducted experiments on the private COVID-DS36 dataset for lung lesion segmentation. Our MC-DC has achieved 87.6% [87.1%, 88.1%], and 92.3% [91.8%, 92.7%] on ground-glass opacity, interstitial infiltration, and lung consolidation, respectively.Conclusions: The experimental results indicate that the proposed MC-DC network exhibits exceptional generalization capability and surpasses other state-of-the-art methods in higher results and lower computational complexity.