Summary of Navigating Uncertainty in Medical Image Segmentation, by Kilian Zepf et al.
Navigating Uncertainty in Medical Image Segmentation
by Kilian Zepf, Jes Frellsen, Aasa Feragen
First submitted to arxiv on: 23 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a study on the selection and evaluation of uncertain segmentation methods in medical imaging. The authors investigate two case studies: prostate segmentation and lung lesion segmentation. They find that simple deterministic models can be sufficient for minimal annotator variation, but also highlight the limitations of the Generalized Energy Distance (GED) in model selection. The study’s findings lead to guidelines for accurately choosing and developing uncertain segmentation models, which integrate aleatoric and epistemic components. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Uncertain segmentation methods are used in medical imaging to analyze images. This paper studies how to choose and evaluate these methods. They look at two examples: segmenting the prostate gland and finding lung lesions. The results show that simple models can work well if there’s not much variation in the data, but a method called Generalized Energy Distance (GED) has its limitations. The study helps develop guidelines for choosing and evaluating uncertain segmentation models, which is important for making medical imaging more accurate. |
