基于类内区域动态解耦的半监督肺气管分割

doi:10.11996/JG.j.2095-302X.2025040763

图学学报 ›› 2025, Vol. 46 ›› Issue (4): 763-774.DOI: 10.11996/JG.j.2095-302X.2025040763

• 图像处理与计算机视觉 • 上一篇下一篇

基于类内区域动态解耦的半监督肺气管分割

汪子宇¹^,²^,³(), 曹维维¹^,², 曹玉柱¹^,², 刘猛⁴, 陈俊⁵^,⁶, 刘兆邦¹^,², 郑健¹^,²^,³()

1.中国科学技术大学生物医学工程学院(苏州)生命科学与医学部，安徽合肥 230026
2.中国科学院苏州生物医学工程技术研究所，江苏苏州 215163
3.威海先进医用材料与高端医疗器械山东省实验室，山东威海 264210
4.苏州波影医疗技术有限公司，江苏苏州 215122
5.苏州大学医学院附属第一医院胸外科，江苏苏州 215129
6.苏州大学医学院附属第一医院胸外科研究所，江苏苏州 215129

收稿日期:2024-12-31 修回日期:2025-02-26 出版日期:2025-08-30 发布日期:2025-08-11
通讯作者:郑健(1984-)，男，研究员，博士。主要研究方向为医学图像分析。E-mail：zhengj@sibet.ac.cn
第一作者:汪子宇(2000-)，男，硕士研究生。主要研究方向为医学图像分割。E-mail：wangziyu_0731@mail.ustc.edu.cn
基金资助:
国家自然科学基金(62371449);国家自然科学基金(U23A20483);山东省实验室项目(SYS202208);苏州市科技计划项目(SYC2022098)

Semi-supervised pulmonary airway segmentation based on dynamically decoupling intra-class regions

WANG Ziyu¹^,²^,³(), CAO Weiwei¹^,², CAO Yuzhu¹^,², LIU Meng⁴, CHEN Jun⁵^,⁶, LIU Zhaobang¹^,², ZHENG Jian¹^,²^,³()

1. School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei Anhui 230026, China
2. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou Jiangsu 215163, China
3. Shandong Laboratory of Advanced Biomaterials and Medical Devices in Weihai, Weihai Shandong 264210, China
4. Suzhou Bowing Medical Technologies Co., Ltd., Suzhou Jiangsu 215122, China
5. Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Medical College of Soochow University, Suzhou Jiangsu 215129, China
6. Institute of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou Jiangsu 215129, China

Received:2024-12-31 Revised:2025-02-26 Published:2025-08-30 Online:2025-08-11
First author：WANG Ziyu (2000-), master student. His main research interest covers medical image segmentation. E-mail：wangziyu_0731@mail.ustc.edu.cn
Supported by:
National Natural Science Foundation of China(62371449);National Natural Science Foundation of China(U23A20483);Shandong Laboratory Project(SYS202208);Suzhou Science and Technology Plan Project(SYC2022098)

摘要/Abstract

摘要：

从计算机断层扫描(CT)图像中精确分割肺气管是诊断和治疗各类肺部疾病的重要前提，但气管复杂的树状结构使得获取用于深度神经网络训练的像素级标注数据极为困难。半监督学习为在有限标注数据下的气管分割提供了新的思路。然而，在气道分割任务中，大气管(气管及其主分支)和小气管(外周细支气管)在体素数量、分支数量和形态结构等方面存在显著的类内差异。严重的类内不平衡问题导致模型易在半监督学习中对占主导地位的分割类别发生过拟合，从而对外周细支气管的表征学习不足、分割精度差，限制了临床应用。针对这一问题，提出了一种新颖的基于单教师双学生三分支网络的半监督肺气管分割框架，在有限的标注数据下实现气管树状结构的精准分割。同时，设计了一个即插即用的动态阈值模块，在网络迭代训练的过程中引导网络识别具有不同分割难度的子区域。此外，还提出了一种新颖的类内区域解耦策略，通过采取不同约束优化方式对不同分割难度的子区域进行表征学习。在2个公开数据集和一个私有数据集上的实验结果表明，该方法在气管分割上优于现有最先进方法，Dice相似系数(DSC)指标达到了91.96%，气管中心线长度(TD)和气管树分支(BD)指标分别达到81.88%和78.32%，实现了CT图像中肺气管快速精准分割。

关键词: 深度学习, 气管分割, 半监督学习, 类内解耦, 动态阈值

Abstract:

Accurate airway segmentation from computed tomography (CT) images served as the foundation for diagnosing and treating various pulmonary diseases. However, the complex tree-like structure rendered pixel-level annotation extremely difficult. Semi-supervised learning methods provide insights for airway segmentation with limited labeled data. However, in airway segmentation tasks, there are significant intra-class differences between the large airway (trachea and main branches) and small airway (peripheral bronchioles) regarding voxel quantity, branch count, and morphological structure. Nevertheless, in airway segmentation tasks, the severe intra-class imbalance problem causes the model being prone to overfitting toward the dominated segmentation classes in semi-supervised learning with limited annotations. This results in insufficient representation learning for peripheral bronchioles, leading to poor segmentation accuracy and limiting clinical applications. To address this issue, a novel semi-supervised pulmonary airway segmentation framework based on a single-teacher dual-student three-branch network was proposed, achieving accurate airway tree-like structure with limited labeled data. In addition, a plug-and-play dynamic threshold module was developed to guide the network in identifying sub-regions with different segmentation difficulties during network training steps. Moreover, a novel intra-class region decoupling strategy was designed, enabling representation learning for sub-regions with different segmentation difficulties through different constraint optimization methods. Experimental results on two public datasets and a real-world dataset demonstrated that the proposed method outperformed the current state-of-the-art methods for airway segmentation. The Dice coefficient achieved 91.96%, while the TD and BD metrics reached 81.88% and 78.32%, respectively, enabling fast and accurate airway segmentation from CT images.

Key words: deep learning, airway segmentation, semi-supervised learning, decoupling intra-class regions, dynamic threshold

中图分类号:

汪子宇, 曹维维, 曹玉柱, 刘猛, 陈俊, 刘兆邦, 郑健. 基于类内区域动态解耦的半监督肺气管分割[J]. 图学学报, 2025, 46(4): 763-774.

WANG Ziyu, CAO Weiwei, CAO Yuzhu, LIU Meng, CHEN Jun, LIU Zhaobang, ZHENG Jian. Semi-supervised pulmonary airway segmentation based on dynamically decoupling intra-class regions[J]. Journal of Graphics, 2025, 46(4): 763-774.

图/表 12

参考文献 32

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方法	标注使用比例		评价指标
方法	有标签	无标签	Dice	TD	BD
3D U-Net	10	0	85.76	67.64	56.18
3D U-Net	20	0	90.65	75.67	65.60
3D U-Net	100	0	93.48	84.38	76.48
MT	10	90	90.77	70.25	58.05
UA-MT	10	90	91.25	75.04	64.29
CPS	10	90	90.53	75.60	65.06
ICT	10	90	91.14	75.95	66.16
URPC	10	90	91.24	76.94	65.78
BCP	10	90	89.10	70.52	59.93
Ours	10	90	91.61	81.34	72.93
MT	20	80	91.46	77.47	67.28
UA-MT	20	80	91.49	77.10	67.56
CPS	20	80	91.41	78.87	69.57
ICT	20	80	91.12	79.89	70.90
URPC	20	80	90.74	78.93	69.01
BCP	20	80	90.55	71.84	61.10
Ours	20	80	91.96	81.88	73.82

方法	标注使用比例		评价指标
方法	有标签	无标签	Dice	TD	BD
3D U-Net	10	0	85.76	67.64	56.18
3D U-Net	20	0	90.65	75.67	65.60
3D U-Net	100	0	93.48	84.38	76.48
MT	10	90	90.77	70.25	58.05
UA-MT	10	90	91.25	75.04	64.29
CPS	10	90	90.53	75.60	65.06
ICT	10	90	91.14	75.95	66.16
URPC	10	90	91.24	76.94	65.78
BCP	10	90	89.10	70.52	59.93
Ours	10	90	91.61	81.34	72.93
MT	20	80	91.46	77.47	67.28
UA-MT	20	80	91.49	77.10	67.56
CPS	20	80	91.41	78.87	69.57
ICT	20	80	91.12	79.89	70.90
URPC	20	80	90.74	78.93	69.01
BCP	20	80	90.55	71.84	61.10
Ours	20	80	91.96	81.88	73.82

方法	标注使用比例		评价指标
方法	有标签	无标签	Dice	TD	BD
3D U-Net	10	0	86.77	59.87	48.32
3D U-Net	20	0	90.97	66.25	54.40
3D U-Net	100	0	92.30	79.89	71.43
MT	10	90	90.99	66.76	55.58
UA-MT	10	90	90.53	67.45	56.49
CPS	10	90	91.27	69.25	58.34
ICT	10	90	91.64	71.33	60.53
URPC	10	90	91.51	71.56	61.52
BCP	10	90	87.99	63.34	52.09
Ours	10	90	91.83	73.36	63.01
MT	20	80	91.66	70.17	59.83
UA-MT	20	80	91.60	71.46	61.01
CPS	20	80	92.24	73.17	62.41
ICT	20	80	92.27	74.56	64.80
URPC	20	80	91.63	73.59	64.46
BCP	20	80	91.05	70.34	59.54
Ours	20	80	92.34	75.80	66.02

方法	标注使用比例		评价指标
方法	有标签	无标签	Dice	TD	BD
3D U-Net	10	0	86.77	59.87	48.32
3D U-Net	20	0	90.97	66.25	54.40
3D U-Net	100	0	92.30	79.89	71.43
MT	10	90	90.99	66.76	55.58
UA-MT	10	90	90.53	67.45	56.49
CPS	10	90	91.27	69.25	58.34
ICT	10	90	91.64	71.33	60.53
URPC	10	90	91.51	71.56	61.52
BCP	10	90	87.99	63.34	52.09
Ours	10	90	91.83	73.36	63.01
MT	20	80	91.66	70.17	59.83
UA-MT	20	80	91.60	71.46	61.01
CPS	20	80	92.24	73.17	62.41
ICT	20	80	92.27	74.56	64.80
URPC	20	80	91.63	73.59	64.46
BCP	20	80	91.05	70.34	59.54
Ours	20	80	92.34	75.80	66.02

方法	标注使用比例		评价指标
方法	有标签	无标签	Dice	TD	BD
3D U-Net	10	0	80.61	49.22	40.37
3D U-Net	20	0	81.42	52.77	42.85
3D U-Net	100	0	83.78	60.56	48.99
MT	10	90	82.78	49.19	40.46
UA-MT	10	90	83.11	53.81	44.53
CPS	10	90	82.51	54.23	43.92
ICT	10	90	82.96	55.56	45.31
URPC	10	90	83.92	55.44	45.81
BCP	10	90	82.80	52.12	42.59
Ours	10	90	83.87	58.98	48.63
MT	20	80	83.64	56.95	46.56
UA-MT	20	80	83.40	57.22	46.73
CPS	20	80	82.78	55.81	45.61
ICT	20	80	83.63	58.80	47.92
URPC	20	80	83.57	58.43	47.91
BCP	20	80	82.84	52.53	43.32
Ours	20	80	83.88	59.40	48.54

基于类内区域动态解耦的半监督肺气管分割

Semi-supervised pulmonary airway segmentation based on dynamically decoupling intra-class regions

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摘要/Abstract

引用本文

使用本文

图/表 12

参考文献 32

相关文章 15

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Metrics

本文评价

数据集	标注使用比例/%		模块			指标/%
数据集	有标注	无标注	基线模型	困难区域	容易区域	Dice	TD	BD
ATM22	10	0				85.76	67.64	56.18
	10	90	√			89.84	73.98	63.35
	10	90	√	√		91.69	78.97	69.31
	10	90	√		√	91.86	80.56	71.84
	10	90	√	√	√	91.61	81.34	72.93
BAS	10	0				86.77	59.87	48.32
	10	90	√			91.16	70.06	59.38
	10	90	√	√		91.89	72.6	62.05
	10	90	√		√	91.96	72.73	62.63
	10	90	√	√	√	91.83	73.36	63.01

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数据集	标注使用比例/%		方法	指标/%
数据集	有标注	无标注	方法	Dice	TD	BD
ATM22	10	0	Fixed Thershold	89.87	71.84	61.15
	10	90	Entropy	91.60	79.30	70.03
	10	90	Prototype	91.97	78.52	69.08
	10	90	Ours	91.61	81.34	72.93
BAS	10	0	Fixed Thershold	85.34	50.87	40.78
	10	90	Entropy	91.26	70.87	60.47
	10	90	Prototype	91.48	71.24	60.21
	10	90	Ours	91.83	73.36	63.01

数据集	标注使用比例/%		方法	指标/%
数据集	有标注	无标注	方法	Dice	TD	BD
ATM22	10	0	Fixed Thershold	89.87	71.84	61.15
	10	90	Entropy	91.60	79.30	70.03
	10	90	Prototype	91.97	78.52	69.08
	10	90	Ours	91.61	81.34	72.93
BAS	10	0	Fixed Thershold	85.34	50.87	40.78
	10	90	Entropy	91.26	70.87	60.47
	10	90	Prototype	91.48	71.24	60.21
	10	90	Ours	91.83	73.36	63.01