Medical Image Segmentation Network Based on Shuffled Feature Encoding and Gated Decoding

LEI Tao; ZHANG Jun-ming; DU Xiao-gang; MIN Chong-dan; YANG Zi-yao

doi:10.12263/DZXB.20231011

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Medical Image Segmentation Network Based on Shuffled Feature Encoding and Gated Decoding

PAPERS | 更新时间：2025-12-11

- Medical Image Segmentation Network Based on Shuffled Feature Encoding and Gated Decoding
- ACTA ELECTRONICA SINICA Vol. 52, Issue 12, Pages: 4142-4152(2024)
- 作者机构：
  
  1.陕西科技大学电子信息与人工智能学院，陕西西安 710021
  2.陕西省人工智能联合实验室（陕西科技大学），陕西西安 710021
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62271296;62201334);Shaanxi Province Outstanding Youth Science Fund(2021JC-47);Shaanxi Province Key Research and Development Program(2022GY-436;2021ZDLGY08-07);Shaanxi Province Innovation Capability Support Program(2020SS-03);Scientific Research Program Funded by Shaanxi Provincial Education Commission(23JP014;23JP022)
- DOI：10.12263/DZXB.20231011
  CLC： TP391;
- Received：27 October 2023，
  
  Revised：2024-08-07，
  
  Published：25 December 2024
- 稿件说明：
移动端阅览
雷涛, 张峻铭, 杜晓刚, 等. 基于混洗特征编码与门控解码的医学图像分割网络[J]. 电子学报, 2024, 52(12): 4142-4152.

LEI Tao, ZHANG Jun-ming, DU Xiao-gang, et al. Medical Image Segmentation Network Based on Shuffled Feature Encoding and Gated Decoding[J]. Acta Electronica Sinica, 2024, 52(12): 4142-4152.
雷涛, 张峻铭, 杜晓刚, 等. 基于混洗特征编码与门控解码的医学图像分割网络[J]. 电子学报, 2024, 52(12): 4142-4152. DOI：10.12263/DZXB.20231011

LEI Tao, ZHANG Jun-ming, DU Xiao-gang, et al. Medical Image Segmentation Network Based on Shuffled Feature Encoding and Gated Decoding[J]. Acta Electronica Sinica, 2024, 52(12): 4142-4152. DOI：10.12263/DZXB.20231011

摘要

针对医学图像分割领域长期存在的多目标尺度变化大和边界模糊以致分割困难的问题，提出了一种新型的基于混洗特征编码和门控解码的双分支混合网络框架用于多器官精准分割.为了充分利用卷积神经网络（Convolutional Neural Network，CNN）在局部信息提取方面和Transformer在长程依赖关系建模方面的优势，采用U-Net和Swin-Unet构建双分支网络.该方法的创新之处在于对不同网络分支的多个阶段学习到的高维特征进行混洗操作，通过双支路通道交叉融合的方式实现局部信息与全局信息的高效融合，加强了双分支网络在不同阶段间的信息交互，从而解决了图像目标轮廓模糊引起的分割精度受限的问题.此外，为了解决多器官尺度变化大的问题，进一步提出了一种全新的基于多尺度特征图的门控解码器（Gated Decoder based on Multi-scale Feature，GDMF）.该解码器能够学习网络不同阶段的多尺度高维特征并进行自适应特征增强，采用注意力机制和特征映射来辅助获取精准目标信息.实验结果表明，与现有主流医学图像分割方法相比，所提方法在ACDC（Automated Cardiac Diagnosis Challenge）和FLARE21（Fast and Low GPU memory Abdominal oRgan sEgmentation challenge 2021）数据集上均表现出更优的性能，有效解决了医学图像中多目标尺度变化大和边界模糊问题.

Abstract

To solve the long-standing problems of the great scale variation in target sizes and blurred boundaries that make segmentation difficult in medical image segmentation

we propose a novel dual-branch hybrid network framework based on feature encoding and gated decoder based on multi-scale feature for accurate multi-organ segmentation. In order to fully exploit the strengths of convolutional neural network (CNN) in local information extraction and transformers in modeling long-range dependency

we employ U-Net and Swin-Unet to construct the dual-branch network. The innovation of this method lies in the shuffling operation of high-dimensional features extracted at multiple stages from different branches of the network. It efficiently integrates local and global information by means of a dual-branch channel cross-fusion

enhancing information interaction between the dual-branch network at different stages. This addresses the limitation in segmentation accuracy caused by the blurring of object contours in images. Additionally

to address the challenge of great scale variation among multiple organs

we introduce a new gated decoder based on multi-scale feature (GDMF) to extract multi-scale high-dimensional features at different stages of the network and perform adaptive feature enhancement

and adopts the attention mechanisms and feature mappings to assist in acquiring accurate target information. The experimental results on automated cardiac diagnosis challenge (ACDC) and fast and low GPU memory abdominal organ segmentation challenge 2021 (FLARE21) datasets demonstrate that our proposed method outperforms existing mainstream medical image segmentation methods and effectively solves the problems of the great scale variation in target sizes and blurred boundary in medical images.

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references

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