564-06-002 ISMRM Abstract

Fast and Reliable Failure Detection for Image Segmentation Using Pairwise Dice Similarity

Primary: Analysis Methods - Segmentation and Detection

Secondary: Neuro - Aging

564-06-002 · Segmentation for Neuro Applications · Wednesday, 13 May, 4:55 PM–5:50 PM · Digital Posters Row E

Keywords: Neurodegeneration Failure Detection Out-Of-Distribution Detection Segmentation Quality

Accepted

Tommaso Di Noto ^1,2,3, Ian Cherabier^1,2,3, Lina Bacha^1,2,3, Punith Bidarakka Venkategowda^4,5, Keerthi Prabhu M⁵, Silvia Pistocchi³, Vincent Dunet³, Attapon Jantarato⁶, Manuela Vaneckova⁷, Emmanuelle Le Bars⁸, Jeremy Deverdun⁸, Nicolas Menjot de Champfleur⁸, Jonathan A Disselhorst^1,2,3, Bénédicte Maréchal^1,2,3

¹Swiss Innovation Hub, Siemens Healthineers International AG, Lausanne, Switzerland

²LTS5, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

³Department of Diagnostic and Interventional Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland

⁴International Institute of Information Technology, Bengaluru, India

⁵Magnetic Resonance, Siemens Healthcare Pvt. Ltd., BENGALURU, India

⁶National Cyclotron and PET Centre, Chulabhorn Hospital, Bangkok, Thailand

⁷Charles University and General University Hospital, Prague, Czech Republic

⁸Department of Neuroradiology, Hospital and University of Montpellier, Montpellier, France

Presenting Author: Tommaso Di Noto

Synopsis

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References

1. Raposo, Hugo. "Intelligent Imaging: A Systematic Review of Artificial Intelligence Techniques in Disease Detection, Segmentation, and Classification." Segmentation, and Classification (May 13, 2024). http://dx.doi.org/10.2139/ssrn.5342645 [doi]

2. Zenk, Maximilian, et al. "Comparative benchmarking of failure detection methods in medical image segmentation: unveiling the role of confidence aggregation." Medical image analysis 101 (2025): 103392. https://doi.org/10.1016/j.media.2024.103392 [doi]

3. Guan, Hao, and Mingxia Liu. "Domain adaptation for medical image analysis: a survey." IEEE Transactions on Biomedical Engineering 69.3 (2021): 1173-118. 10.1109/TBME.2021.3117407 [doi]

4. Li, Kang, Lequan Yu, and Pheng-Ann Heng. "Towards reliable cardiac image segmentation: Assessing image-level and pixel-level segmentation quality via self-reflective references." Medical Image Analysis 78 (2022): 102426. https://doi.org/10.1016/j.media.2022.102426 [doi]

5. Venkategowda PB, Di Noto T, Prabhu M K, et al. Establishing Brain Reference Ranges in Indian Population: Comparison with Westerners and Validation for Parkinsonism Differential Diagnosis. In: Proceedings of the 34th Annual Meeting of ISMRM. 2025. https://archive.ismrm.org/2025/1502.html

6. Cardoso, M. Jorge, et al. "Monai: An open-source framework for deep learning in healthcare." arXiv preprint arXiv:2211.02701 (2022). https://doi.org/10.48550/arXiv.2211.02701 [doi]

7. Schafer, Ronald W. "What is a savitzky-golay filter?" IEEE Signal processing magazine 28.4 (2011): 111-117. 10.1109/MSP.2011.941097 [doi]

8. Morelli, John N., et al. "An image-based approach to understanding the physics of MR artifacts." Radiographics 31.3 (2011): 849-866. https://doi.org/10.1148/rg.313105115 [doi]

9. Cosarinsky, Matias, et al. "In-Context Reverse Classification Accuracy: Efficient Estimation of Segmentation Quality without Ground-Truth." arXiv preprint arXiv:2503.04522 (2025). https://doi.org/10.48550/arXiv.2503.04522 [doi]

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http://echo.ismrm.org/p/ISMRM2026/564-06-002