MHSU-Net: A more versatile neural network for medical image segmentation. (September 2021)

• Record Type: Journal Article
• Authors: Ma, Hao; Zou, Yanni; Liu, Peter X.
• Is Part Of: Computer methods and programs in biomedicine. Volume 208(2021)
• Publication Date: 2021-09
• Subjects: Medical image segmentation -- Convolutional neural network -- U-Net -- Multiscale and context; 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.2021.106230
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: An improved convolutional module MCB is proposed. A hybrid down-sampling module HDSB is proposed to reduce the feature loss. The Skip Connection+ structure is proposed to capture more context information. Abstract: Background and objective: Medical image segmentation plays an important role in clinic. Recently, with the development of deep learning, many convolutional neural network (CNN)-based medical image segmentation algorithms have been proposed. Among them, U-Net is one of the most famous networks. However, the standard convolutional layers used by U-Net limit its capability to capture abundant features. Additionally, the consecutive maximum pooling operations in U-Net cause certain features to be lost. This paper aims to improve the feature extraction capability of U-Net and reduce the feature loss during the segmentation process. Meanwhile, the paper also focuses on improving the versatility of the proposed segmentation model. Methods: Firstly, in order to enable the model to capture richer features, we have proposed a novel multiscale convolutional block (MCB). MCB adopts a wider and deeper structure, which can be applied to different types of segmentation tasks. Secondly, a hybrid down-sampling block (HDSB) has been proposed to reduce the feature loss via replacing the maximum pooling layer. Thirdly, we have proposed a context module (CIF) based on atrous convolution and SKNet to extract sufficient context information. Finally, we combined the CIF module with Skip Connection of U-Net, and further proposed the Skip Connection+ structure. Results: We name the proposed network MHSU-Net. MHSU-Net has been evaluated on three different datasets, including lung, cell contour, and pancreas. Experimental results demonstrate that MHSU-Net outperforms U-Net and other state-of-the-art models under various evaluation metrics, and owns greater potential in clinical applications. Conclusions: The proposed modules can greatly improve the feature extraction capability of the segmentation model and effectively reduce the feature loss during the segmentation process. MHSU-Net can also be applied to different types of medical image segmentation tasks.