Unsupervised 3D Medical Image Segmentation With Sparse Radiation Measurement

XIAOFAN Yu¹; LANLAN Zou²; WENQI Gu²; JUN Cai; BIN Kang²; KANG Ding

doi:10.11999/JEIT250841

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XIAOFAN Yu¹, LANLAN Zou², WENQI Gu², JUN Cai, BIN Kang², KANG Ding. Unsupervised 3D Medical Image Segmentation With Sparse Radiation Measurement[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250841

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XIAOFAN Yu¹, LANLAN Zou², WENQI Gu², JUN Cai, BIN Kang², KANG Ding. Unsupervised 3D Medical Image Segmentation With Sparse Radiation Measurement[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250841

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Unsupervised 3D Medical Image Segmentation With Sparse Radiation Measurement

doi: 10.11999/JEIT250841 cstr: 32379.14.JEIT250841

XIAOFAN Yu¹^{1, 2, 3, 4},
LANLAN Zou²^{1, 2, 3, 4},
WENQI Gu²^{1, 2, 3, 4},
JUN Cai^{1, 2, 3, 4},
BIN Kang²^{1, 2, 3, 4},
KANG Ding^{1, 2, 3, 4
,
,}

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: National Natural Science Foundation of China under Grants (62171232), Key Projects of the Jiangsu Provincial Clinical Medicine Innovation Center for Anorectal Diseases (GCCXZX-2021), Key Projects of the Nanjing Municipal Special Fund for Health Science and Technology Development (ZKX24046), TCM Monitoring and Statistics from the National Administration of Traditional Chinese Medicine Statistics Center

Accepted Date: 2025-11-05
Rev Recd Date: 2025-11-05

Available Online: 2025-11-15

Abstract

Abstract

Objective Three-dimensional medical image segmentation is recognized as a pivotal task in modern medical image analysis. Compared with two-dimensional imaging, it captures the spatial morphology of organs and lesions more comprehensively and provides clinicians with detailed structural information, thereby facilitating early disease screening, personalized surgical planning, and treatment evaluation. With rapid advances in artificial intelligence, three-dimensional segmentation is increasingly regarded as a key technology for diagnostic support, precision therapy, and intraoperative navigation. However, existing methods such as SwinUNETR-v2 and UNETR++ rely heavily on large-scale voxel-level annotations, which incur high annotation costs and limit clinical applicability. Moreover, high-quality segmentation frequently depends on multi-view projections to obtain complete volumetric data, resulting in increased radiation exposure and physiological burden for patients. Consequently, segmentation under sparse radiation measurements is posed as an important challenge. Neural Attenuation Fields (NAF) have recently been proposed as a promising approach for low-dose reconstruction by recovering linear attenuation coefficient fields from sparse views. Nevertheless, their potential for three-dimensional segmentation remains largely unexplored. To address this gap, a unified framework named NA-SAM3D is proposed, which integrates NAF-based reconstruction with interactive segmentation to achieve unsupervised 3D segmentation under sparse-view conditions, thereby reducing annotation dependence and improving boundary perception. Methods The proposed framework is designed in two stages. In the first stage, sparse-view reconstruction is performed using NAF to generate a continuous three-dimensional attenuation coefficient tensor from sparse X-ray projections. Ray sampling and positional encoding are applied to arbitrary 3D points, and the encoded features are passed into a multi-layer perceptron (MLP) to predict linear attenuation coefficients that serve as input for subsequent segmentation. In the second stage, interactive segmentation is conducted. A three-dimensional image encoder is used to extract high-dimensional features from the attenuation coefficient tensor, while clinician-provided point prompts indicate 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 preliminary masks. Because point prompts provide only local positional cues, boundary ambiguity and mask over-expansion may occur. To mitigate these issues, a Density-Guided Module (DGM) is introduced at the decoder output stage: NAF-derived attenuation coefficients are converted into a density-aware attention map, which is fused with preliminary mask predictions to strengthen tissue boundary perception and improve segmentation accuracy in complex anatomical regions. Results and Discussions NA-SAM3D is validated on a self-constructed colorectal cancer dataset comprising 299 patient cases (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). Experimental results show that NA-SAM3D achieves overall better performance than the mainstream unsupervised 3D segmentation methods based on full radiation observation (SAM-MED series) and reaches accuracy comparable to or even higher than the fully supervised model SwinUNETR-v2. Compared with SAM-MED3D, NA-SAM3D improves the Dice on the LCTSC dataset by more than 3%, while HD95 and ASD decrease by 5.29 mm and 1.32 mm, respectively, demonstrating better boundary localization and surface consistency. Compared with the sparse-field-based segmentation method SA3D, NA-SAM3D achieves higher Dice scores on all three datasets (Table 1). Compared with the fully supervised model 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 verifying its ability to recover structural details and boundary information under sparse-view conditions (Table 2). Although the overall performance of NA-SAM3D is slightly lower than that of the fully supervised UNETR++ model, it still demonstrates strong competitiveness and good generalization under label-free inference. Qualitative results show 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 verifies that in colorectal, lung, and liver regions, NA-SAM3D achieves better structural continuity and boundary preservation than SAM-MED2D and SAM-MED3D (Fig. 5). The Density-Guided Module further improves 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 3D medical image segmentation framework, NA-SAM3D, is proposed, which integrates NAF reconstruction with interactive 3D segmentation to achieve high-precision segmentation under sparse radiation measurements. The Density-Guided Module effectively leverages attenuation coefficient priors to enhance recognition of complex lesion boundaries. Experimental results demonstrate that the method approaches the performance of fully supervised approaches under unsupervised inference, with an average 2.0% Dice improvement, indicating substantial practical value and clinical potential for low-dose imaging and complex anatomical segmentation. Future work will focus on optimizing the model for additional anatomical regions and evaluating its practical application in preoperative planning.
- Sparse radiation measurement,
- 3D medical image segmentation,
- Neural attenuation fields,
- Anorectal diseases

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References(27)

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