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Biomedical image segmentation based on full-Resolution network

Authors:

Peng DuanAuthors Info & Claims

Volume 153, Issue C

Pages 232 - 238

https://doi.org/10.1016/j.patrec.2021.11.017

Published: 01 January 2022 Publication History

Highlights

•

A new CNN structure is designed to extract two complementary features of image.

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A new feature is used to remedy the missing image details in semantic feature.

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A full-feature extraction module is designed to extract the new feature.

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A fusion module is used to aggregate image features selectively and effectively.

Abstract

Convolutional neural networks (CNN) has been widely used in biomedical image segmentation (BIS) tasks for its remarkable feature representation capability, and most of existing CNN-based segmentation networks leverage a down-sampling operation to achieve larger acceptance domain. However, down-sampling operations could inevitably loss the detailed information of images which is very important for the BIS task. In this paper, we propose a full-resolution biomedical image segmentation network(FRNet) that could maintain the integrated detailed information of image while keeping sufficient semantic information and large receptive field. Specifically, the basic semantic feature and non-destructive feature are employed to represent the semantic and detailed information of images, respectively. A backbone network and a new full-feature extraction branch are conducted to extract those two kinds of complementary features. Furthermore, a novel feature fusion module is designed to integrate those complementary features to achieve non-destructive description of images. Finally, in order to further improve the description ability of the integrated feature, a Densely connected Atrous Spatial Pyramid Pooling(DenseASPP) module is arranged at the end of our proposed FRNet to extract the multiscale information of images. Thorough experimental results on several available databases demonstrate the effectiveness and advancement of FRNet.

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Cited By

Cheng JTian SYu LLiu SWang CRen YLu HZhu M(2022)DDU-NetApplied Soft Computing10.1016/j.asoc.2022.109297126:COnline publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1016/j.asoc.2022.109297

Index Terms

Biomedical image segmentation based on full-Resolution network
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation
        Video segmentation
      2. Computer vision tasks
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

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

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Information & Contributors

Information

Published In

cover image Pattern Recognition Letters

Pattern Recognition Letters Volume 153, Issue C

Jan 2022

261 pages

ISSN:0167-8655

Issue’s Table of Contents

Elsevier B.V.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 January 2022

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Citations

Cited By

Cheng JTian SYu LLiu SWang CRen YLu HZhu M(2022)DDU-NetApplied Soft Computing10.1016/j.asoc.2022.109297126:COnline publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1016/j.asoc.2022.109297

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