Uncertainty Propagation for Echocardiography Clinical Metric Estimation via Contour Sampling
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866910834591006720 |
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| author | Judge, Thierry Bernard, Olivier Kim, Woo-Jin Cho Gomez, Alberto Beqiri, Arian Chartsias, Agisilaos Jodoin, Pierre-Marc |
| author_facet | Judge, Thierry Bernard, Olivier Kim, Woo-Jin Cho Gomez, Alberto Beqiri, Arian Chartsias, Agisilaos Jodoin, Pierre-Marc |
| contents | Echocardiography plays a fundamental role in the extraction of important clinical parameters (e.g. left ventricular volume and ejection fraction) required to determine the presence and severity of heart-related conditions. When deploying automated techniques for computing these parameters, uncertainty estimation is crucial for assessing their utility. Since clinical parameters are usually derived from segmentation maps, there is no clear path for converting pixel-wise uncertainty values into uncertainty estimates in the downstream clinical metric calculation. In this work, we propose a novel uncertainty estimation method based on contouring rather than segmentation. Our method explicitly predicts contour location uncertainty from which contour samples can be drawn. Finally, the sampled contours can be used to propagate uncertainty to clinical metrics. Our proposed method not only provides accurate uncertainty estimations for the task of contouring but also for the downstream clinical metrics on two cardiac ultrasound datasets. Code is available at: https://github.com/ThierryJudge/contouring-uncertainty. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_12713 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Uncertainty Propagation for Echocardiography Clinical Metric Estimation via Contour Sampling Judge, Thierry Bernard, Olivier Kim, Woo-Jin Cho Gomez, Alberto Beqiri, Arian Chartsias, Agisilaos Jodoin, Pierre-Marc Computer Vision and Pattern Recognition Echocardiography plays a fundamental role in the extraction of important clinical parameters (e.g. left ventricular volume and ejection fraction) required to determine the presence and severity of heart-related conditions. When deploying automated techniques for computing these parameters, uncertainty estimation is crucial for assessing their utility. Since clinical parameters are usually derived from segmentation maps, there is no clear path for converting pixel-wise uncertainty values into uncertainty estimates in the downstream clinical metric calculation. In this work, we propose a novel uncertainty estimation method based on contouring rather than segmentation. Our method explicitly predicts contour location uncertainty from which contour samples can be drawn. Finally, the sampled contours can be used to propagate uncertainty to clinical metrics. Our proposed method not only provides accurate uncertainty estimations for the task of contouring but also for the downstream clinical metrics on two cardiac ultrasound datasets. Code is available at: https://github.com/ThierryJudge/contouring-uncertainty. |
| title | Uncertainty Propagation for Echocardiography Clinical Metric Estimation via Contour Sampling |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.12713 |