Navigating Uncertainty in Medical Image Segmentation

• URL: http://arxiv.org/abs/2407.16367v1
• Date: Tue, 23 Jul 2024 10:21:18 GMT
• Title: Navigating Uncertainty in Medical Image Segmentation
• Authors: Kilian Zepf, Jes Frellsen, Aasa Feragen,
• Abstract summary: We address the selection and evaluation of uncertain segmentation methods in medical imaging. We present two case studies: prostate segmentation, illustrating that for minimal annotator variation simple deterministic models can suffice. Our findings lead to guidelines for accurately choosing and developing uncertain segmentation models.
• Score: 13.12913475818328
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We address the selection and evaluation of uncertain segmentation methods in medical imaging and present two case studies: prostate segmentation, illustrating that for minimal annotator variation simple deterministic models can suffice, and lung lesion segmentation, highlighting the limitations of the Generalized Energy Distance (GED) in model selection. Our findings lead to guidelines for accurately choosing and developing uncertain segmentation models, that integrate aleatoric and epistemic components. These guidelines are designed to aid researchers and practitioners in better developing, selecting, and evaluating uncertain segmentation methods, thereby facilitating enhanced adoption and effective application of segmentation uncertainty in practice.

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