Navigating Uncertainty in Medical Image Segmentation

Autor:	Zepf, Kilian, Frellsen, Jes, Feragen, Aasa
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Published in the conference proceedings of the 21st IEEE International Symposium on Biomedical Imaging (ISBI 2024)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.16367 Zobrazit plný text záznamu View this record from Arxiv