基于双尺度纠缠互补学习的半监督医学图像分割算法

doi:10.13229/j.cnki.jdxbgxb.20260106

吉林大学学报(工学版) ›› 2026, Vol. 56 ›› Issue (10): 2695-2705.doi: 10.13229/j.cnki.jdxbgxb.20260106

• 计算机科学与技术 • 上一篇

基于双尺度纠缠互补学习的半监督医学图像分割算法

王雪¹(),朱永发¹,李洪宇²(),周炜鹏³,刘太辉¹

^1.北华大学计算机科学技术学院，吉林省吉林市 132013
^2.北华大学药学院，吉林省吉林市 132013
^3.吉林大学第二医院放射科，长春 130012

收稿日期:2026-02-03 出版日期:2026-10-01 发布日期:2026-10-09
通讯作者: 李洪宇 E-mail:wxue@beihua.edu.cn;26149236@qq.com
作者简介:王雪（1982-），女，副教授，博士. 研究方向：机器学习与图像处理. E-mail：wxue@beihua.edu.cn
基金资助:
吉林省自然科学基金面上项目(20250102224JC);吉林省科技厅科技发展计划项目(YDZJ202201ZYTS605);吉林省发改委预算内基本建设项目(2022C046-4);北华大学校管科研项目(BHBS2024-12)

Semi-supervised medical image segmentation algorithm based on dual-scale entangled complementary learning

Xue WANG¹(),Yong-fa ZHU¹,Hong-yu LI²(),Wei-peng ZHOU³,Tai-hui LIU¹

^1.College of Computer Science and Technology，Beihua University，Jilin 132013，China
^2.College of Pharmacy，Beihua University，Jilin 132013，China
^3.Department of Radiology，The Second Hospital，Jilin University，Changchun 130012，China

Received:2026-02-03 Online:2026-10-01 Published:2026-10-09
Contact: Hong-yu LI E-mail:wxue@beihua.edu.cn;26149236@qq.com

摘要/Abstract

摘要：

半监督学习是缓解医学图像分割标注数据稀缺问题的关键技术，但现有方法对形态异质性强、边界模糊的复杂医学图像分割效果仍不足。为此，本文提出一种基于双尺度纠缠互补学习的半监督分割算法。通过双尺度纠缠互补解码模块建立双尺度特征间的协同互补关系，实现结构信息的互补建模。其中，跨层融合编码模块强化浅层细节与深层语义特征的交互；多维特征优化模块增强解码阶段的空间结构与边缘细节重建能力。结合监督损失与扰动、尺度一致性损失，实现半监督训练。实验结果表明：本文算法在结肠镜息肉、皮肤病变及脑部肿瘤数据集上取得了较优的分割性能，在分割精度、边界平滑性及跨测试集泛化能力上均表现出良好的稳定性。

关键词: 医学图像分割, 半监督学习, 纠缠互补解码, 跨层融合编码, 多维特征优化

Abstract:

Semi-supervised learning serves as a core technique to address the scarcity of labeled data in medical image segmentation. However， existing methods struggle to achieve satisfactory segmentation results for complex medical images with high morphological heterogeneity and blurred boundaries. This paper proposes a semi-supervised segmentation algorithm based on Dual-Scale Entangled Complementary Learning. A Dual-Scale Entangled Complementary Decoder is constructed to establish collaborative complementary relationships between dual-scale features and realize complementary modeling of structural information. Specifically， the Cross-level Fusion Encoder Module strengthens the interaction between shallow detailed features and deep semantic features， while the Multi-Dimensional Feature Optimization Module improves the reconstruction capability of spatial structures and boundary details during decoding. Supervised loss together with perturbation and scale consistency losses are adopted to implement semi-supervised training. Experimental results demonstrate that the proposed algorithm achieves competitive segmentation performance on datasets of colon polyps， skin lesions and brain tumors， and maintains favorable stability in segmentation accuracy， boundary smoothness and cross-test-set generalization ability.

Key words: medical image segmentation, semi-supervised learning, entangled complementary decoder, cross-level fusion encoder, multi-dimensional feature optimization

中图分类号:

TP391

王雪,朱永发,李洪宇,周炜鹏,刘太辉. 基于双尺度纠缠互补学习的半监督医学图像分割算法[J]. 吉林大学学报(工学版), 2026, 56(10): 2695-2705.

Xue WANG,Yong-fa ZHU,Hong-yu LI,Wei-peng ZHOU,Tai-hui LIU. Semi-supervised medical image segmentation algorithm based on dual-scale entangled complementary learning[J]. Journal of Jilin University(Engineering and Technology Edition), 2026, 56(10): 2695-2705.

图/表 12

图1

图2

图3

表1

CVC-ClinicDB和Kvasir数据集上的实验对比结果"

数据集	方法	评价指标
数据集	方法	mDice	mIoU	$F β ω$	$S α$	MAE
CVC-ClinicDB	URPC^［25］	0.781	0.692	0.777	0.823	0.026
	SSNet^［26］	0.809	0.712	0.785	0.858	0.023
	MCNet^［27］	0.796	0.714	0.780	0.842	0.025
	DCNet^［28］	0.808	0.730	0.805	0.851	0.025
	MPDC^［5］	0.817	0.751	0.812	0.837	0.018
	DECseg^［29］	0.843	0.792	0.839	0.863	0.010
	本文	0.877	0.820	0.872	0.901	0.013
Kvasir	URPC^［25］	0.739	0.644	0.727	0.783	0.067
	SSNet^［26］	0.769	0.670	0.750	0.807	0.064
	MCNet^［27］	0.779	0.681	0.758	0.808	0.063
	DCNet^［28］	0.790	0.695	0.768	0.813	0.061
	MPDC^［5］	0.801	0.712	0.785	0.834	0.052
	DECseg^［29］	0.863	0.797	0.858	0.883	0.039
	本文	0.877	0.805	0.870	0.888	0.038

表1

图4

表2

CVC-ColonDB和ETIS数据集上的实验对比结果"

数据集	方法	评价指标
数据集	方法	mDice	mIoU	$F β ω$	$S α$	MAE
CVC-ColonDB	URPC^［25］	0.461	0.379	0.441	0.586	0.054
	SSNet^［26］	0.514	0.409	0.462	0.664	0.073
	MCNet^［27］	0.477	0.394	0.448	0.614	0.064
	DCNet^［28］	0.534	0.461	0.525	0.612	0.033
	MPDC^［5］	0.575	0.498	0.566	0.633	0.029
	DECseg^［29］	0.724	0.650	0.714	0.773	0.025
	本文	0.727	0.646	0.713	0.798	0.032
ETIS	URPC^［25］	0.353	0.290	0.337	0.483	0.029
	SSNet^［26］	0.406	0.317	0.358	0.598	0.040
	MCNet^［27］	0.488	0.404	0.450	0.624	0.032
	DCNet^［28］	0.419	0.358	0.406	0.509	0.022
	MPDC^［5］	0.433	0.364	0.420	0.534	0.021
	DECseg^［29］	0.570	0.500	0.544	0.640	0.016
	本文	0.674	0.592	0.642	0.781	0.017

表2

表3

ISIC 2018数据集上的实验对比结果"

数据集	方法	评价指标
数据集	方法	mDice	mIoU	$F β ω$	$S α$	MAE
ISIC2018	URPC^［25］	0.847	0.761	0.820	0.826	0.085
	SSNet^［26］	0.865	0.783	0.837	0.840	0.076
	MCNet^［27］	0.855	0.771	0.831	0.834	0.081
	DCNet^［28］	0.853	0.774	0.842	0.836	0.083
	MPDC^［5］	0.847	0.758	0.817	0.827	0.081
	DECseg^［29］	0.867	0.786	0.825	0.844	0.072
	本文	0.874	0.793	0.832	0.848	0.071

表3

图5

表4

脑肿瘤数据集上的实验对比结果"

数据集	方法	评价指标
数据集	方法	mDice	mIoU	$F β ω$	$S α$	MAE
BrainMRI	URPC^［25］	0.694	0.591	0.682	0.782	0.011
	SSNet^［26］	0.698	0.590	0.687	0.793	0.012
	MCNet^［27］	0.706	0.606	0.701	0.784	0.010
	DCNet^［28］	0.712	0.616	0.705	0.785	0.010
	MPDC^［5］	0.676	0.586	0.671	0.742	0.011
	DECseg^［29］	0.811	0.724	0.822	0.859	0.006
	本文	0.814	0.720	0.823	0.871	0.007

表4

图6

图7

表5

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评价指标	模块
评价指标	Baseline	+ECDM	+ECDM+CFEM	+ECDM+CFEM+MDFO
Parameters（M）	120.38	284.34	298.54	207.02
FLOPs（G）	90.89	174.57	199.26	159.17

基于双尺度纠缠互补学习的半监督医学图像分割算法

Semi-supervised medical image segmentation algorithm based on dual-scale entangled complementary learning

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