U-Net combined with multi-scale attention mechanism for liver segmentation in CT images

Jiawei Wu,Songlin Zuo,Yiyin Chen,Jiantuan Duan,Hui Wang,Shengqiang Zhou,Jiang Luo,Deguang Wang,Weiqin Sun

doi:10.1186/s12911-021-01649-w

Abstract

BackgroundThe liver is an important organ that undertakes the metabolic function of the human body. Liver cancer has become one of the cancers with the highest mortality. In clinic, it is an important work to extract the liver region accurately before the diagnosis and treatment of liver lesions. However, manual liver segmentation is a time-consuming and boring process. Not only that, but the segmentation results usually varies from person to person due to different work experience. In order to assist in clinical automatic liver segmentation, this paper proposes a U-shaped network with multi-scale attention mechanism for liver organ segmentation in CT images, which is called MSA-UNet. Our method makes a new design of U-Net encoder, decoder, skip connection, and context transition structure. These structures greatly enhance the feature extraction ability of encoder and the efficiency of decoder to recover spatial location information. We have designed many experiments on publicly available datasets to show the effectiveness of MSA-UNet. Compared with some other advanced segmentation methods, MSA-UNet finally achieved the best segmentation effect, reaching 98.00% dice similarity coefficient (DSC) and 96.08% intersection over union (IOU).

Highlights

The liver is an important organ that undertakes the metabolic function of the human body
This study proposed a solution to the problem of liver segmentation in Computed tomography (CT) images
A network structure, MSA-UNet, which was suitable for liver organ segmentation in CT images, was proposed

Summary

Introduction

The liver is an important organ that undertakes the metabolic function of the human body. In order to assist in clinical automatic liver segmentation, this paper proposes a U-shaped network with multi-scale attention mechanism for liver organ segmentation in CT images, which is called MSA-UNet. Our method makes a new design of U-Net encoder, decoder, skip connection, and context transition structure. Our method makes a new design of U-Net encoder, decoder, skip connection, and context transition structure These structures greatly enhance the feature extraction ability of encoder and the efficiency of decoder to recover spatial location information. Effective segmentation of liver tumor regions based on CT imaging technology has an important clinical value. Compared with manual segmentation methods or traditional semi-automatic segmentation algorithms, CNNs have efficient feature extraction capability. It can perform fully automatic end-to-end training of data without too much empirical

Methods

Results

Discussion

Conclusion