research-article

FDCT: : Fusion-Guided Dual-View Consistency Training for semi-supervised tissue segmentation on MRI

Authors:

Rongchang Zhao,

Hailan ShenAuthors Info & Claims

Volume 160, Issue C

https://doi.org/10.1016/j.compbiomed.2023.106908

Published: 01 June 2023 Publication History

Abstract

Accurate tissue segmentation on MRI is important for physicians to make diagnosis and treatment for patients. However, most of the models are only designed for single-task tissue segmentation, and tend to lack generality to other MRI tissue segmentation tasks. Not only that, the acquisition of labels is time-consuming and laborious, which remains a challenge to be solved. In this study, we propose the universal Fusion-Guided Dual-View Consistency Training(FDCT) for semi-supervised tissue segmentation on MRI. It can obtain accurate and robust tissue segmentation for multiple tasks, and alleviates the problem of insufficient labeled data. Especially, for building bidirectional consistency, we feed dual-view images into a single-encoder dual-decoder structure to obtain view-level predictions, then put them into a fusion module to generate image-level pseudo-label. Moreover, to improve boundary segmentation quality, we propose the Soft-label Boundary Optimization Module(SBOM). We have conducted extensive experiments on three MRI datasets to evaluate the effectiveness of our method. Experimental results demonstrate that our method outperforms the state-of-the-art semi-supervised medical image segmentation methods.

Highlights

•

We propose a universal semi-supervised tissue segmentation framework on MRI called FDCT.

•

We improve the CBAM based on the task of this paper, which brings a boost to the network performance.

•

We propose the SBOM that generates boundary soft-label, which improves the quality of boundary segmentation.

•

Experiments on three datasets show that our method outperforms state-of-the-art semi-supervised medical image segmentation methods.

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Index Terms

FDCT: Fusion-Guided Dual-View Consistency Training for semi-supervised tissue segmentation on MRI
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

Information

Published In

cover image Computers in Biology and Medicine

Computers in Biology and Medicine Volume 160, Issue C

Jun 2023

680 pages

ISSN:0010-4825

Issue’s Table of Contents

Elsevier Ltd.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 June 2023

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