A novel MCF-Net: Multi-level context fusion network for 2D medical image segmentation. (November 2022)

• Record Type: Journal Article
• Authors: Liu, Lizhu; Liu, Yexin; Zhou, Jian; Guo, Cheng; Duan, Huigao
• Is Part Of: Computer methods and programs in biomedicine. Volume 226(2022)
• Publication Date: 2022-11
• Subjects: Deep learning -- U-Net -- MCF-Net -- Hybrid attention-based residual atrous convolution module -- Multi-scale feature memory module -- Multi-receptive field fusion module -- Medical image segmentation; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.107160
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Medical image segmentation is a crucial step in the clinical applications for diagnosis and analysis of some diseases. The multi-level contextual information integration capability and the feature extraction ability are often insufficient. We present a novel multi-level context fusion network to fuse multi-scale contextual information, to effectively extract multi-receptive field features. The proposed MCF-Net was evaluated on the ISIC 2018, DRIVE, BUSI, and Kvasir-SEG datasets. The experimental results show that MCF-Net is very competitive with other U-Net models, and it offers tremendous potential as a general-purpose deep learning model for 2D medical image segmentation. Abstract: Medical image segmentation is a crucial step in the clinical applications for diagnosis and analysis of some diseases. U-Net-based convolution neural networks have achieved impressive performance in medical image segmentation tasks. However, the multi-level contextual information integration capability and the feature extraction ability are often insufficient. In this paper, we present a novel multi-level context fusion network (MCF-Net) to improve the performance of U-Net on various segmentation tasks by designing three modules, hybrid attention-based residual atrous convolution (HARA) module, multi-scale feature memory (MSFM) module, and multi-receptive field fusion (MRFF) module, to fuse multi-scale contextual information. HARA module was proposed to effectively extract multi-receptive field features by combing atrous spatial pyramid pooling and attention mechanism. We further design the MSFM and MRFF modules to fuse features of different levels and effectively extract contextual information. The proposed MCF-Net was evaluated on the ISIC 2018, DRIVE, BUSI, and Kvasir-SEG datasets, which have challenging images of many sizes and widely varying anatomy. The experimental results show that MCF-Net is very competitive with other U-Net models, and it offers tremendous potential as a general-purpose deep learning model for 2D medical image segmentation.