Assessing Test-Time Variability for Interactive 3D Medical Image Segmentation with Diverse Point Prompts
Open accessAbstract
Interactive segmentation model leverages prompts from users to produce robust segmentation. This advancement is facilitated by prompt engineering, where interactive prompts serve as strong priors during test-time. However, this is an inherently subjective and hard-to-reproduce process. The variability in user expertise and inherently ambiguous boundaries in medical images can lead to inconsistent prompt selections, potentially affecting segmentation accuracy. This issue has not yet been extensively explored for medical imaging. In this paper, we assess the test-time variability for interactive medical image segmentation with diverse point prompts. For a given target region, the point is classified into three sub-regions: boundary, margin, and center. Our goal is to identify a straightforward and efficient approach for optimal prompt selection during test-time based on three considerations: (1) benefits of additional prompts, (2) effects of prompt placement, and (3) strategies for optimal prompt selection. We conduct extensive experiments on the public Medical Segmentation Decathlon dataset for challenging colon tumor segmentation task. We suggest an optimal strategy for prompt selection during test-time, supported by comprehensive results. The code is publicly available at https://github.com/MedICL-VU/variability
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Test-time performance of interactive 3D medical segmentation can vary with the placement and selection strategy of point prompts.
“Prior analysis of interactive 3D medical segmentation has shown that testtime performance can vary with the placement and selection strategy of point prompts [18].”
From Correction-aware interactive 3D tumor segmentation with sparse and revisable prompts (2026) · cites this paper as [18]
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Cites
Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.-Y., et al. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00371 · Cited by 11,953 · see citing works
Segment anything in medical images
Ma, J., He, Y., Li, F., Han, L., You, C., & Wang, B. (2024) · Nature Communications · doi:10.1038/s41467-024-44824-z · Cited by 2,913 · see citing works
Interactive medical image segmentation using deep learning with image-specific fine tuning
Wang, G., Li, W., Zuluaga, M. A., Pratt, R., Patel, P. A., Aertsen, M., Doel, T., David, A. L., Deprest, J., Ourselin, S., et al. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2791721 · Cited by 843 · see citing works
Structure Boundary Preserving Segmentation for Medical Image With Ambiguous Boundary
Lee, H. J., Kim, J. U., Lee, S., Kim, H. G., & Ro, Y. M. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00487 · Cited by 180 · see citing works
3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentation
Gong, S., Zhong, Y., Ma, W., Li, J., Wang, Z., Zhang, J., Heng, P.-A., & Dou, Q. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103324 · Cited by 156 · see citing works
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Research works printed but not linked: 12
- Han Liu, Dewei Hu, Hao Li, and Ipek Oguz, "Medical image segmentation using deep learning," in Machine Learning for Brain Disorders, pp. 391-434. Springer, 2023.
- Jiacheng Wang, Hao Li, Han Liu, Dewei Hu, Daiwei Lu, Keejin Yoon, Kelsey Barter, Francesca Bagnato, and Ipek Oguz, "Ssl2 self-supervised learning meets semi- supervised learning: multiple clerosis segmentation in 7t-mri from large-scale 3t-mri," in Medical Imaging 2023: Image Processing. SPIE, 2023, vol. 12464, pp. 126-136.
- Christos Matsoukas, Johan Fredin Haslum, Moein Sorkhei, Magnus Söderberg, and Kevin Smith, "What makes transfer learning work for medical images: Fea- ture reuse & other factors," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 9225-9234.
- Junde Wu, Rao Fu, Huihui Fang, Yuanpei Liu, Zhaowei Wang, Yanwu Xu, Yueming Jin, and Tal Arbel, "Med- ical sam adapter: Adapting segment anything model for medical image segmentation," arXiv preprint arXiv:2304.12620, 2023. · arXiv
- Hao Li, Han Liu, Dewei Hu, Jiacheng Wang, and Ipek Oguz, "Promise: Prompt-driven 3d medical image seg- mentation using pretrained image foundation models," arXiv preprint arXiv:2310.19721, 2023. · arXiv
- Guoyao Deng, Ke Zou, Kai Ren, Meng Wang, Xue- dong Yuan, Sancong Ying, and Huazhu Fu, "Sam- u: Multi-box prompts triggered uncertainty estimation for reliable sam in medical image," arXiv preprint arXiv:2307.04973, 2023. · arXiv
- Yizhe Zhang, Tao Zhou, Peixian Liang, and Danny Z Chen, "Input augmentation with sam: Boosting med- ical image segmentation with segmentation foundation model," arXiv preprint arXiv:2304.11332, 2023. · arXiv
- Arne Schmidt, Pablo Morales-Álvarez, and Rafael Molina, "Probabilistic modeling of inter-and intra- observer variability in medical image segmentation," in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 21097-21106.
- Xing Yao, Han Liu, Dewei Hu, Daiwei Lu, Ange Lou, Hao Li, Ruining Deng, Gabriel Arenas, Baris Oguz, Nadav Schwartz, et al., "False negative/positive con- trol for sam on noisy medical images," arXiv preprint arXiv:2308.10382, 2023. · arXiv
- Dongjie Cheng, Ziyuan Qin, Zekun Jiang, Shaoting Zhang, Qicheng Lao, and Kang Li, "Sam on med- ical images: A comprehensive study on three prompt modes," arXiv preprint arXiv:2305.00035, 2023. · arXiv
- Zheng Lin, Zhao Zhang, Lin-Zhuo Chen, Ming-Ming Cheng, and Shao-Ping Lu, "Interactive image segmen- tation with first click attention," in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 13339-13348.
- Michela Antonelli, Annika Reinke, Spyridon Bakas, Keyvan Farahani, Annette Kopp-Schneider, Bennett A Landman, Geert Litjens, Bjoern Menze, Olaf Ron- neberger, Ronald M Summers, et al., "The medical seg- mentation decathlon," Nature communications, vol. 13, no. 1, pp. 4128, 2022.
Cited by
Sorted by citations
PRISM: A Promptable and Robust Interactive Segmentation Model with Visual Prompts
Li, H., Liu, H., Hu, D., Wang, J., & Oguz, I. (2024) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-72384-1_37 · Cited by 12 · see citing works
Self-citationCorrection-aware interactive 3D tumor segmentation with sparse and revisable prompts
Li, H., & Li, H. (2026) · The Visual Computer · doi:10.1007/s00371-026-04560-5
“Prior analysis of interactive 3D medical segmentation has shown that test-time performance can vary with the placement and selection strategy of point prompts [18].”
Self-citation
- Authors
- Li, H., Liu, H., Hu, D., Wang, J., & Oguz, I.
- Affiliations
- Vanderbilt University (US)
- Vanderbilt University
- Venue
- 2024 IEEE International Symposium on Biomedical Imaging (ISBI)
- Publisher
- IEEE
- Published
- 2024-05-27
- Pages
- 1-5
- OpenAlex
- W4401749153
- Cited by
- 9
- References
- 5
- Citations by year
- 2026: 2 · 2024: 7
- FWCI
- 1.86
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE International Symposium on Biomedical Imaging (ISBI) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- National Science Foundation (2220401)
- National Institutes of Health
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- interactive segmentation, point prompts, prompt selection, medical image segmentation, Medical Segmentation Decathlon
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