融合反事实推理与轻量化设计的半监督脑肿瘤分割网络

樊亚文; 王潮远; 王鑫; 张鑫晨; 周全

doi:10.11999/JEIT251130

融合反事实推理与轻量化设计的半监督脑肿瘤分割网络

doi: 10.11999/JEIT251130 cstr: 32379.14.JEIT251130

南京邮电大学通信与信息工程学院南京 210003

基金项目: 国家自然科学基金项目(62476139)，江苏省研究生科研与实践创新计划项目(SJCX25_0308)

详细信息

作者简介:
樊亚文：女，讲师，研究方向为机器学习和因果推理及其在医学图像处理中的应用

王潮远：男，硕士研究生，研究方向为医学图像处理

王鑫：男，硕士研究生，研究方向为医学图像处理

张鑫晨：男，硕士研究生，研究方向为医学图像处理

周全：男，教授，研究方向为机器学习，图像语义分割

通讯作者:
樊亚文　ywfan@njupt.edu.xn

中图分类号: TN911.73; TP391.41
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出版历程
- 修回日期: 2026-04-15
- 录用日期: 2026-04-15
- 网络出版日期: 2026-04-30

A Lightweight Semi-Supervised Brain Tumor Segmentation Network with Counterfactual Reasoning

National Engineering Research Center of Communications and Networking, Nanjing University of Posts Telecommunications, Nanjing 210003, China

Funds: The National Natural Science Foundation of China(62476139), Postgraduate Research & Practice Innovation Program of Jiangsu Province (SJCX25_0308)

摘要

摘要: 针对脑肿瘤分割任务中标注样本稀缺、高计算开销以及病灶边界模糊问题，从模型结构与半监督机制两个角度出发，提出一种融合轻量化骨干网络与反事实推理的半监督脑肿瘤分割方法，旨在同时提升分割精度与模型部署效率。在网络结构设计方面，基于解剖结构一致性先验，构建了参数共享的多模态融合编码器-解码器架构，在保证分割性能的同时显著降低模型参数量与计算开销，使其适用于资源受限的临床应用场景。在半监督训练策略方面，利用教师-学生模型预测结果构建反事实样本，设计了一种结合像素级分割一致性与特征级语义稳定性的反事实推理损失函数，从而充分挖掘未标注数据的结构信息。在BraTS2021数据集上的实验结果表明，即使仅使用 10% 的标注数据，半监督模型在主要分割指标上平均达到约 94% 的全监督性能，同时在边界细节和小病灶识别性能方面均优于现有主流方法。
- 脑肿瘤分割 /
- 多模态融合 /
- 轻量化模型 /
- 半监督学习 /
- 反事实推理
Abstract: Objective Brain tumor segmentation plays an essential role in clinical diagnosis and treatment planning. However, creating reliable labels for medical images is costly and time-consuming, making it difficult to obtain large annotated datasets. To address this challenge, we propose a semi-supervised brain tumor segmentation approach that combines a lightweight backbone network with a counterfactual reasoning strategy. The study aims to improve segmentation accuracy while keeping the model efficient enough for real clinical use. Methods We design a multi-modal encoder–decoder network with shared parameters to reduce the model size and computation. The network incorporates anatomical structure consistency as prior knowledge, helping it better align with the underlying brain anatomy. During training, a teacher–student framework is used to generate counterfactual samples from their predictions. These samples guide the learning of unlabeled data through a counterfactual loss function that enforces pixel-level consistency and feature-level stability. This strategy helps the model capture useful structural information from unlabeled scans without relying on artificial data augmentations that may distort tumor boundaries. Results and Discussions Experiments conducted on the BraTS2019 and BraTS2021 datasets demonstrate that the proposed method consistently outperforms other models under limited-label conditions. On BraTS2019, our approach achieves the best performance in terms of DSC (66.06%), and its IoU (53.16%) is comparable to other models. More importantly, it attains the lowest HD95 of 7.60 mm, representing an 11% and 6% reduction compared to UNet3D and LightMU-Net, respectively (Table 2 and 3). On BraTS2019, the proposed method improves DSC and IoU by 4–7% on average, while reducing HD95 by 0.6 mm (Table 4 and 5). The model is also highly efficient, with only 1.657M parameters, 0.4402T FLOPs, and an inference time of 0.0937 s per frame (Table 6). These results confirm that the optimized design effectively balances segmentation accuracy, computational efficiency, and clinical usability. The improvements come from both the lightweight network design and the counterfactual mechanism, which encourages the model to learn anatomically meaningful representations. Conclusions The proposed framework provides a simple yet effective solution for semi-supervised brain tumor segmentation. It offers a good balance between accuracy, efficiency, and interpretability, and demonstrates how causal reasoning can be practically integrated into medical image analysis.
- Brain tumor segmentation /
- Multimodal fusion /
- Lightweight /
- Semi-supervised learning /
- Counterfactual Reasoning

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图 1 轻量化的基于反事实损失的半监督脑肿瘤分割网络

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图 2 轻量化多模态融合分割模型

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图 3 轻量化多模态特征融合模块

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图 4 结构因果模型

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图 5 脑肿瘤分割标签

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图 6 MRI四种模态及标签2D切片展示

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图 7 基线模式下分割结果2D可视化

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图 8 半监督模式下分割结果2D可视化

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图 9 所提出模型在半监督与全监督模型下分割结果的对比

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图 10 对比其它损失函数在肿瘤分割任务中的结果可视化

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表 1 反事实标签

教师模型(z^t)	学生模型(z^t)	反事实标签(z^t)
ET(3)	ED(2)	NET(1)
ED(2)	ET(3)	NET(1)
ED(2)	NET(1)	ET(3)
NET(1)	ED(2)	ET(3)
NET(1)	ET(3)	ED(2)
ET(3)	NET(1)	ED(2)

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表 2 数据集详情

数据集	样本总数	训练集		验证集	测试集
数据集	样本总数	有标签	无标签	验证集	测试集
BraTS19	335	27	241	33	34
BraTS21	1251	100	900	125	126

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表 3 BraTS 2019数据集上的结果对比(DSC和IoU指标对比)

模型	DSC(%)↑			IoU(%)↑
模型	ET	TC	WT	ET	TC	WT
UNet3D(2016)	51.07±0.26	60.13±0.15	78.25±0.21	37.53±0.26	47.03±0.33	66.15±0.20
UXNet3D(2023)	58.86±0.19	61.55±0.08	77.15±0.15	46.14±0.25	48.46±0.06	65.32±0.77
LightMUnet(2024)	56.61±0.01	59.72±0.02	80.65±0.03	43.72±0.01	46.43±0.02	69.37±0.04
Ours	55.40±0.18	62.64±0.13	80.13±0.05	41.68±0.14	49.50±0.07	68.32±0.13

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表 4 BraTS 2019数据集上的结果对比(HD95和平均指标对比)

模型	HD95(mm)↓			平均值
模型	ET	TC	WT	DSC(%)↑	IoU(%)↑	HD95(mm)↓
UNet3D(2016)	9.33±0.13	9.72±0.24	6.67±0.01	63.15±0.20	50.23±0.26	8.57±0.12
UXNet3D(2023)	12.12±0.21	11.86±0.31	8.95±0.10	65.85±0.14	53.30±0.56	10.97±0.20
LightMUnet(2024)	9.14±0.01	9.53±0.02	5.61±0.19	65.66±0.02	53.17±0.02	8.09±0.07
Ours	9.67±0.10	8.33±0.17	4.79±0.14	66.06±0.12	53.16±0.11	7.60±0.13

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表 5 BraTS 2021数据集上的结果对比(DSC和IoU)

模型	模式	有标签	无标签	DSC(%)↑			IoU(%)↑
模型	模式	有标签	无标签	ET	TC	WT	ET	TC	WT
UNet3D (2016)	base	100	0	64.54±0.20	68.29±0.11	82.05±0.13	54.67±0.21	55.25±0.14	71.62±0.18
UNet3D (2016)	semi	100	900	69.08±0.18	70.83±0.14	87.89±0.09	56.29±0.21	58.20±0.15	79.16±0.15
Attn-Unet (2018)	base	100	0	63.48±0.04	69.57±0.01	85.63±0.02	49.56±0.04	57.18±0.02	75.77±0.04
Attn-Unet (2018)	semi	100	900	68.84±0.34	69.79±0.08	86.10±0.07	55.88±0.52	57.52±0.10	76.36±0.17
UXNet3D (2023)	base	100	0	65.20±0.03	67.33±0.06	82.18±0.05	54.40±0.05	54.41±0.06	71.26±0.01
UXNet3D (2023)	semi	100	900	68.88±0.05	74.85±0.04	86.95±0.06	60.15±0.06	62.95±0.04	77.82±0.09
LightMUnet (2024)	base	100	0	62.20±0.03	66.53±0.06	82.38±0.15	50.40±0.08	54.61±0.06	71.56±0.01
LightMUnet (2024)	semi	100	900	67.88±0.07	73.75±0.09	85.65±0.06	54.15±0.06	62.25±0.04	76.52±0.21
Ours	base	100	0	65.56±0.05	70.04±0.12	82.77±0.02	54.85±0.29	56.81±0.15	71.81±0.01
	semi	100	900	69.30±0.16	77.30±0.16	88.28±0.05	60.46±0.19	66.05±0.26	79.70±0.08
	full	1000	0	74.63±0.24	83.54±0.14	90.36±0.02	63.43±0.32	74.73±0.22	82.88±0.02

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表 6 BraTS 2021数据集上的结果对比(HD95和平均指标)

模型	模式	有标签	无标签	HD95(mm)↓			平均值
模型	模式	有标签	无标签	ET	TC	WT	DSC(%)↑	IoU(%)↑	HD95(mm)↓
UNet3D (2016)	base	100	0	3.76±0.16	3.63±0.04	1.42±0.04	71.63±0.15	60.51±0.18	2.94±0.08
UNet3D (2016)	semi	100	900	2.85±0.04	2.81±0.02	1.09±0.01	75.93±0.14	64.55±0.17	2.25±0.02
Attn-Unet (2018)	base	100	0	4.46±0.01	4.89±0.01	3.82±0.01	72.89±0.02	60.84±0.03	4.46±0.01
Attn-Unet (2018)	semi	100	900	3.90±0.17	4.37±0.03	3.18±0.06	74.91±0.16	63.25±0.26	3.81±0.09
UXNet3D (2023)	base	100	0	3.70±0.04	3.51±0.24	1.71±0.04	71.57±0.05	60.02±0.04	2.97±0.11
UXNet3D (2023)	semi	100	900	2.81±0.02	2.73±0.02	1.11±0.06	76.90±0.05	66.97±0.06	2.22±0.03
LightMUnet (2024)	base	100	0	4.07±0.04	4.01±0.13	2.17±0.14	70.37±0.08	58.86±0.05	3.42±0.10
LightMUnet (2024)	semi	100	900	3.09±0.03	3.13±0.06	1.81±0.11	75.76±0.07	64.31±0.10	2.68±0.07
Ours	base	100	0	3.42±0.03	3.51±0.01	1.05±0.01	72.79±0.06	61.16±0.15	2.66±0.02
	semi	100	900	2.68±0.10	2.55±0.11	1.03±0.02	78.29±0.12	68.74±0.18	2.09±0.08
	full	1000	0	2.08±0.05	2.13±0.01	1.00±0.01	82.84±0.13	73.68±0.19	1.74±0.02

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表 7 性能指标对比结果

模型	参数量(M)	计算量(T)	推理时间 (s)
UNet3D	6.5301	0.5643	4.0046
Attn-Unet	6.5920	0.5765	4.5678
UXNet3D	53.0594	1.3941	0.2780
LightM-Unet	6.1528	0.2343	3.3296
Ours	1.6570	0.4402	0.0937

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表 8 不同损失函数结果对比

损失函数	分割区域	指标得分
损失函数	分割区域	DSC(%)↑	IOU(%)↑	HD95(mm)↓
MSE	ET	69.07±0.24	56.09±0.29	2.74±0.09
	TC	75.28±0.24	63.24±0.34	2.75±0.06
	WT	87.65±0.01	78.72±0.04	1.05±0.03
NCE	ET	69.21±0.18	56.23±0.33	2.73±0.12
	TC	76.43±0.26	63.69±0.48	2.79±0.11
	WT	87.31±0.09	78.32±0.07	1.06±0.08
Ours	ET	69.30±0.16	56.46±0.19	2.68±0.10
	TC	77.30±0.16	66.05±0.26	2.55±0.11
	WT	88.28±0.05	79.70±0.08	1.03±0.02

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参考文献(24)

[1]	GHADIMI D J, VAHDANI A M, KARIMI H, et al. Deep learning-based techniques in glioma brain tumor segmentation using multi-parametric MRI: A review on clinical applications and future outlooks[J]. Journal of Magnetic Resonance Imaging, 2025, 61(3): 1094–1109. doi: 10.1002/JMRI.29543.
[2]	江宗康, 吕晓钢, 张建新, 等. MRI脑肿瘤图像分割的深度学习方法综述[J]. 中国图象图形学报, 2020, 25(2): 215–228. doi: 10.11834/jig.190173. JIANG Zongkang, LV Xiaogang, ZHANG Jianxin, et al. Review of deep learning methods for MRI brain tumor image segmentation[J]. Journal of Image and Graphics, 2020, 25(2): 215–228. doi: 10.11834/jig.190173.
[3]	SOOMRO T A, ZHENG Lihong, AFIFI A J, et al. Image segmentation for MR brain tumor detection using machine learning: A review[J]. IEEE Reviews in Biomedical Engineering, 2023, 16: 70–90. doi: 10.1109/RBME.2022.3185292.
[4]	张印辉, 张金凯, 何自芬, 等. 全局感知与稀疏特征关联图像级弱监督病理图像分割[J]. 电子与信息学报, 2024, 46(9): 3672–3682. doi: 10.11999/JEIT240364. ZHANG Yinhui, ZHANG Jinkai, HE Zifen, et al. Global perception and sparse feature associate image-level weakly supervised pathological image segmentation[J]. Journal of Electronics & Information Technology, 2024, 46(9): 3672–3682. doi: 10.11999/JEIT240364.
[5]	丁建睿, 张听, 刘家栋, 等. 融合邻域注意力和状态空间模型的医学视频分割算法[J]. 电子与信息学报, 2025, 47(5): 1582–1595. doi: 10.11999/JEIT240755. DING Jianrui, ZHANG Ting, LIU Jiadong, et al. A medical video segmentation algorithm integrating neighborhood attention and state space model[J]. Journal of Electronics & Information Technology, 2025, 47(5): 1582–1595. doi: 10.11999/JEIT240755.
[6]	LEE H H, BAO Shunxing, HUO Yuankai, et al. 3D UX-Net: A large kernel volumetric convnet modernizing hierarchical transformer for medical image segmentation[C]. Proceedings of the 11th International Conference on Learning Representations, Kigali, Rwanda, 2023.
[7]	HATAMIZADEH A, TANG Yucheng, NATH V, et al. UNETR: Transformers for 3D medical image segmentation[C]. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, Waikoloa, USA, 2022: 1748–1758. doi: 10.1109/WACV51458.2022.00181.
[8]	PUCH S, SÁNCHEZ I, HERNÁNDEZ A, et al. Global planar convolutions for improved context aggregation in brain tumor segmentation[C]. Proceedings of 4th International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Granada, Spain, 2018: 393–405. doi: 10.1007/978-3-030-11726-9_35.
[9]	RONNEBERGER O, FISCHER P, and BROX T. U-Net: Convolutional networks for biomedical image segmentation[C]. Proceedings of 18th International Conference on Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, Munich, Germany, 2015: 234–241. doi: 10.1007/978-3-319-24574-4_28.
[10]	刘海超, 宋丽娟. 多模态MRI脑肿瘤分割方法的特征融合技术综述[J]. 计算机工程与应用, 2024, 60(23): 28–48. doi: 10.3778/j.issn.1002-8331.2402-0087. LIU Haichao and SONG Lijuan. Review of feature fusion techniques for multimodal MRI brain tumor segmentation methods[J]. Computer Engineering and Applications, 2024, 60(23): 28–48. doi: 10.3778/j.issn.1002-8331.2402-0087.
[11]	LIU Yu, SHI Yu, MU Fuhao, et al. Multimodal MRI volumetric data fusion with convolutional neural networks[J]. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 4006015. doi: 10.1109/TIM.2022.3184360.
[12]	韩汶杞, 蒋雯, 耿杰, 等. 原型对齐与拓扑一致性约束下的多模态半监督遥感图像语义分割[J]. 电子与信息学报, 2025, 47(12): 4714–4727. doi: 10.11999/JEIT251115. HAN Wenqi, JIANG Wen, GENG Jie, et al. PATC: Prototype alignment and topology-consistent pseudo-supervision for multimodal semi-supervised semantic segmentation of remote sensing images[J]. Journal of Electronics & Information Technology, 2025, 47(12): 4714–4727. doi: 10.11999/JEIT251115.
[13]	ZHANG Zheng, YIN Guanchun, ZHANG Bo, et al. A semantic knowledge complementarity based decoupling framework for semi-supervised class-imbalanced medical image segmentation[C]. 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, USA, 2025: 25940–25949. doi: 10.1109/CVPR52734.2025.02416.
[14]	TARVAINEN A and VALPOLA H. Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results[C]. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, USA, 2017: 1195–1204.
[15]	SOHN K, BERTHELOT D, LI Chunliang, et al. FixMatch: Simplifying semi-supervised learning with consistency and confidence[C]. Proceedings of the 34th International Conference on Neural Information Processing Systems, Vancouver, Canada, 2020: 51.
[16]	ASSEFA M, NASEER M, GANAPATHI I I, et al. DyCON: Dynamic uncertainty-aware consistency and contrastive learning for semi-supervised medical image segmentation[C]. 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, USA, 2025: 30850–30860. doi: 10.1109/CVPR52734.2025.02873.
[17]	CHI Hanyang, PANG Jian, ZHANG Bingfeng, et al. Adaptive bidirectional displacement for semi-supervised medical image segmentation[C]. 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, USA, 2024: 4070–4080. doi: 10.1109/CVPR52733.2024.00390.
[18]	HITCHCOCK C and PEARL J. Causality: Models, reasoning and inference[J]. The Philosophical Review, 2001, 110(4): 639–641. doi: 10.2307/3182612.
[19]	JONES C, CASTRO D C, DE SOUSA RIBEIRO F, et al. A causal perspective on dataset bias in machine learning for medical imaging[J]. Nature Machine Intelligence, 2024, 6(2): 138–146. doi: 10.1038/s42256-024-00797-8.
[20]	OUYANG Cheng, CHEN Chen, LI Surui, et al. Causality-inspired single-source domain generalization for medical image segmentation[J]. IEEE Transactions on Medical Imaging, 2023, 42(4): 1095–1106. doi: 10.1109/TMI.2022.3224067.
[21]	MIAO Juzheng, CHEN Cheng, LIU Furui, et al. CauSSL: Causality-inspired semi-supervised learning for medical image segmentation[C]. Proceedings of the IEEE/CVF International Conference on Computer Vision, Paris, France, 2023: 21369–21380. doi: 10.1109/ICCV51070.2023.01959.
[22]	QU Jiaqi, XIAO Xiang, WEI Xunbin, et al. A causality-inspired generalized model for automated pancreatic cancer diagnosis[J]. Medical Image Analysis, 2024, 94: 103154. doi: 10.1016/j.media.2024.103154.
[23]	WANG Sihan, LI Lei, and ZHUANG Xiahai. AttU-NET: Attention U-Net for brain tumor segmentation[C]. Proceedings of 7th International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2021: 302–311. doi: 10.1007/978-3-031-09002-8_27. (查阅网上资料,未找到本条文献出版地信息,请确认).
[24]	LIAO Weibin, ZHU Yinghao, WANG Xinyuan, et al. LightM-UNet: Mamba assists in lightweight UNet for medical image segmentation[J]. arXiv preprint arXiv: 2403.05246, 2024. doi: 10.48550/arXiv.2403.05246. (查阅网上资料,不确定本文献类型是否正确,请确认).