面向稀疏辐射观测的无监督三维医学图像分割方法

俞晓帆; 邹兰兰; 顾文琦; 蔡君; 康彬; 丁康

doi:10.11999/JEIT250841

面向稀疏辐射观测的无监督三维医学图像分割方法

doi: 10.11999/JEIT250841 cstr: 32379.14.JEIT250841

俞晓帆¹,
邹兰兰²,
顾文琦²,
蔡君⁴,
康彬²,
丁康^3, ,

1.
南京邮电大学通信与信息工程学院南京 210003
2.
南京邮电大学物联网学院南京 210003
3.
南京市中医院全国肛肠诊疗中心南京 210022
4.
南京中医药大学人工智能与信息技术学院南京 210023

基金项目: 国家自然科学基金(62171232)，江苏省中医肛肠疾病临床医学创新中心重点项目(GCCXZX-2021)，南京市卫生科技发展专项资金重点项目(ZKX24046)，国家中医药管理局监测统计中心2025 年度中医药监测统计研究课题(2025JCTJE2)，国家中医药管理局规划财务司项目(GZY-GCS-2025-006)

详细信息

作者简介:
俞晓帆：男，教授，研究方向为智能信号处理

邹兰兰：女，硕士生，研究方向为医学图像处理

顾文琦：女，硕士生，研究方向为计算机视觉

蔡君：女，讲师，研究方向为医学信号处理

康彬：男，副教授，研究方向为计算机视觉

丁康：男，教授，研究方向为肛肠病临床研究

通讯作者:
丁康　fsyy00237@njucm.edu.cn

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- 被引次数: 0
出版历程
- 收稿日期: 2025-09-01
- 修回日期: 2025-11-05
- 录用日期: 2025-11-05
- 网络出版日期: 2025-11-15

Unsupervised 3D Medical Image Segmentation With Sparse Radiation Measurement

1.
School of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
2.
School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
3.
National Center for Anorectal Diseases, Nanjing Hospital of Traditional Chinese Medicine, Nanjing 210022, China
4.
School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing 210023, China

Funds: The National Natural Science Foundation of China (62171232), The Key Projects of the Jiangsu Provincial Clinical Medicine Innovation Center for Anorectal Diseases (GC-CXZX-2021), The Key Projects of the Nanjing Municipal Special Fund for Health Science and Technology Development (ZKX24046), The TCM Monitoring and Statistics from the National Administration of Traditional Chinese Medicine Statistics Center (2025JCTJE2), National Administration of Traditional Chinese Medicine Planning and Finance Department Project (GZY-GCS-2025-006)

摘要

摘要: 神经衰减场是一种具有前景的三维医学图像重建方法，此方法利用稀疏辐射测量实现与完整观察相接近的重构精度。该文提出一种无监督三维医学影像分割方法，将无监督分割与神经衰减场集成为一个端到端的网络架构。具体而言，所提网络架构包括两个阶段：稀疏测量重建和交互式三维图像分割。两个阶段可通过联合学习自适应实现互惠优化。为解决类似肛肠等复杂病灶中边界模糊和区域过度扩展的难题，所提三维分割网络的交互式三维分割阶段设计了密度引导模块，有效利用衰减系数的先验知识，调节密度感知的注意力机制，提升三维分割泛化性能。通过与南京市中医院合作构建的结直肠癌数据集以及两个公开数据集上的大量实验证明所提方法的优越性，例如与基于全辐射观测的SAM-MED3D算法相比，所提网络仅使用14%稀疏观测值，在3个数据集的平均 Dice 系数提升 2.0%。
- 稀疏辐射测量 /
- 三维医学图像分割 /
- 神经衰减场 /
- 肛肠疾病
Abstract: Objective Three-dimensional medical image segmentation is a central task in medical image analysis. Compared with two-dimensional imaging, it captures organ and lesion morphology more completely and provides detailed structural information, supporting early disease screening, personalized surgical planning, and treatment assessment. With advances in artificial intelligence, three-dimensional segmentation is viewed as a key technique for diagnostic support, precision therapy, and intraoperative navigation. However, methods such as SwinUNETR-v2 and UNETR++ depend on extensive voxel-level annotations, which create high annotation costs and restrict clinical use. High-quality segmentation also often requires multi-view projections to recover full volumetric information, increasing radiation exposure and patient burden. Segmentation under sparse radiation measurements is therefore an important challenge. Neural Attenuation Fields (NAF) have recently been introduced for low-dose reconstruction by recovering linear attenuation coefficient fields from sparse views, yet their suitability for three-dimensional segmentation remains insufficiently examined. To address this limitation, a unified framework termed NA-SAM3D is proposed, integrating NAF-based reconstruction with interactive segmentation to enable unsupervised three-dimensional segmentation under sparse-view conditions, reduce annotation dependence, and improve boundary perception. Methods The framework is designed in two stages. In the first stage, sparse-view reconstruction is performed with NAF to generate a continuous three-dimensional attenuation coefficient tensor from sparse X-ray projections. Ray sampling and positional encoding are applied to arbitrary three-dimensional points, and the encoded features are forwarded to a Multi-Layer Perceptron (MLP) to predict linear attenuation coefficients that serve as input for segmentation. In the second stage, interactive segmentation is performed. A three-dimensional image encoder extracts high-dimensional features from the attenuation coefficient tensor, and clinician-provided point prompts specify regions of interest. These prompts are embedded into semantic features by an interactive user module and fused with image features to guide the mask decoder in producing initial masks. Because point prompts provide only local positional cues, boundary ambiguity and mask expansion may occur. To address these issues, a Density-Guided Module (DGM) is introduced at the decoder output stage. NAF-derived attenuation coefficients are transformed into a density-aware attention map, which is fused with the initial masks to strengthen tissue-boundary perception and improve segmentation accuracy in complex anatomical regions. Results and Discussions NA-SAM3D is evaluated on a self-constructed colorectal cancer dataset comprising 299 patient cases (collected in collaboration with Nanjing Hospital of Traditional Chinese Medicine) and on two public benchmarks: the Lung CT Segmentation Challenge (LCTSC) and the Liver Tumor Segmentation Challenge (LiTS). The results show that NA-SAM3D achieves overall better performance than mainstream unsupervised three-dimensional segmentation methods based on full radiation observation (SAM-MED series) and reaches accuracy comparable to, or in some cases higher than, the fully supervised SwinUNETR-v2. Compared with SAM-MED3D, NA-SAM3D increases the Dice on the LCTSC dataset by more than 3%, while HD95 and ASD decrease by 5.29 mm and 1.32 mm, respectively, indicating improved boundary localization and surface consistency. Compared with the sparse-field-based method SA3D, NA-SAM3D achieves higher Dice scores on all three datasets (Table 1). Compared with the fully supervised SwinUNETR-v2, NA-SAM3D reduces HD95 by 1.28 mm, and the average Dice is only 0.3% lower. Compared with SA3D, NA-SAM3D increases the average Dice by about 6.6% and reduces HD95 by about 11 mm, further confirming its capacity to restore structural details and boundary information under sparse-view conditions (Table 2). Although the overall performance remains slightly lower than that of the fully supervised UNETR++ model, NA-SAM3D still shows strong competitiveness and good generalization under label-free inference. Qualitative analysis shows that in complex pelvic and intestinal regions, NA-SAM3D produces clearer boundaries and higher contour consistency (Fig. 3). On public datasets, segmentation of the lung and liver also shows superior boundary localization and contour integrity (Fig. 4). Three-dimensional visualization further confirms that in colorectal, lung, and liver regions, NA-SAM3D achieves stronger structural continuity and boundary preservation than SAM-MED2D and SAM-MED3D (Fig. 5). The DGM further enhances boundary sensitivity, increasing Dice and mIoU by 1.20% and 3.31% on the self-constructed dataset, and by 4.49 and 2.39 percentage points on the LiTS dataset (Fig. 6). Conclusions An unsupervised three-dimensional medical image segmentation framework, NA-SAM3D, is presented, integrating NAF-based reconstruction with interactive segmentation to achieve high-precision segmentation under sparse radiation measurements. The DGM effectively uses attenuation coefficient priors to enhance boundary recognition in complex lesion regions. Experimental results show that the framework approaches the performance of fully supervised methods under unsupervised inference and yields an average Dice improvement of 2.0%, indicating strong practical value and clinical potential for low-dose imaging and complex anatomical segmentation. Future work will refine the model for additional anatomical regions and assess its practical use in preoperative planning.
- Sparse radiation measurement /
- 3D medical image segmentation /
- Neural Attenuation Fields (NAF) /
- Anorectal diseases

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图 1 所提无监督三维分割方法与传统监督分割方法对比示例

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图 2 NA-SAM3D 框架总体结构。该框架包含两个主要阶段：(1)基于稀疏视图的三维重建；(2)交互式三维分割。其中密度引导模块为分割阶段的关键组成部分，用于增强边界区域的识别能力。

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图 3 不同分割方法在自建结直肠癌数据集上的二维切片结果对比

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图 4 不同分割方法在公共数据集上的二维切片结果对比

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图 5 不同分割方法在三维可视化结果上的对比

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图 6 在引入与未引入密度引导模块条件下，不同数据集的Dice与mIoU性能对比

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图 7 不同交互点数量下模型平均 Dice 值和方差的变化趋势

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图 8 不同稀疏视角数量条件下的平均 Dice 值和方差的变化趋势

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图 9 不同采样点数量条件下的平均 Dice 值和方差的变化趋势

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表 1 不同数据集的定量结果对比,(表中加粗值表示该列指标的最优结果，HD95 与 ASD 的单位均为 mm)

方法	LCTSC				LiTS				自建数据集
方法	DICE	mIoU	HD95	ASD	DICE	mIoU	HD95	ASD	DICE	mIoU	HD95	ASD
Swinunetr-v2^[5]	0.7812	0.6285	18.54	3.66	0.8865	0.8033	10.36	3.27	0.7598	0.6721	17.28	3.71
UNETR++^[6]	0.8171	0.7003	11.20	3.04	0.9126	0.8312	10.08	3.23	0.7835	0.6894	9.70	2.75
SAM-MED2D^[18]	0.5805	0.4090	24.89	5.13	0.8623	0.7577	12.94	3.86	0.7196	0.6361	26.88	5.40
SA3D^[21]	0.6931	0.5389	23.48	4.91	0.8027	0.7123	26.81	5.92	0.7235	0.6108	25.14	5.73
SAM-MED3D^[22]	0.7623	0.6257	21.21	4.69	0.8511	0.7741	17.00	4.22	0.7448	0.6426	18.25	4.05
本文	0.7946	0.6296	15.92	3.37	0.8629	0.7924	12.79	3.31	0.7610	0.6733	13.61	3.05

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表 2 不同模型的均值与方差比较(表中加粗值表示该列指标的最优结果)

方法	平均值				方差
方法	DICE	mIoU	HD95	ASD	DICE	mIoU	HD95	ASD
Swinunetr-v2^[5]	0.8092	0.7013	15.39	3.55	0.0031	0.0055	12.93	0.04
UNETR++^[6]	0.8377	0.7403	10.33	2.94	0.0030	0.0042	0.41	0.02
SAM-MED2D^[18]	0.7208	0.6009	21.57	4.80	0.0132	0.0209	37.90	0.45
SA3D^[21]	0.7398	0.6207	25.14	5.52	0.0021	0.0051	1.84	0.19
SAM-MED3D^[22]	0.7861	0.6808	18.82	4.32	0.0022	0.0044	3.11	0.07
本文	0.8061	0.6984	14.11	3.24	0.0018	0.0047	1.76	0.02

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表 3 基于自建数据集微调下的交叉域评估结果,(表中加粗值表示该列指标的最优结果)

方法	LCTSC				LiTS
方法	DICE	mIoU	HD95	ASD	DICE	mIoU	HD95	ASD
SAM-MED2D^[18]	0.4645	0.3672	27.21	7.96	0.6753	0.5782	28.90	7.22
SAM-MED3D^[22]	0.7018	0.5622	23.83	5.76	0.6921	0.6368	22.82	6.38
本文	0.7442	0.5894	18.06	4.55	0.7231	0.6549	16.12	4.71

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表 4 不同重建方法实验结果对比

三维重建方法	正常投影 DICE (avg)	正常投影 mIoU (avg)	加入噪声投影 DICE (avg)	加入噪声投影 mIoU (avg)
NAF^[12]	0.8061	0.6984	0.7935	0.6811
FDK^[25]	0.7351	0.5966	0.7083	0.5644
SART^[26]	0.7168	0.5863	0.6791	0.5580
NeRF^[11]	0.7550	0.6144	0.7335	0.5916

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