MISm: A medical image segmentation metric for evaluation of weak labeled data

Autor:	Hartmann, D, Schmid, V, Meyer, P, Soto Rey, I, Müller, D, Kramer, F
Jazyk:	angličtina
Rok vydání:	2022
Předmět:	FOS: Computer and information sciences biomedical image segmentation evaluation ddc: 610 Computer Vision and Pattern Recognition (cs.CV) Medicine and health Computer Science - Computer Vision and Pattern Recognition performance assessment medical image analysis
Zdroj:	67. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS), 13. Jahreskongress der Technologie-und Methodenplattform für die vernetzte medizinische Forschung e.V. (TMF); 20220821-20220825; sine loco [digital]; DOCAbstr. 21 /20220819/
DOI:	10.3205/22gmds031
Popis:	Performance measures are an important tool for assessing and comparing different medical image segmentation algorithms. Unfortunately, the current measures have their weaknesses when it comes to assessing certain edge cases. These limitations arouse when images with a very small region of interest or without a region of interest at all are assessed. As a solution for these limitations, we propose a new medical image segmentation metric: MISm. To evaluate MISm, the popular metrics in the medical image segmentation and MISm were compared using images of magnet resonance tomography from several scenarios. In order to allow application in the community and reproducibility of experimental results, we included MISm in the publicly available evaluation framework MISeval: https://github.com/frankkramer-lab/miseval/tree/master/miseval Comment: GitHub: https://github.com/frankkramer-lab/miseval/tree/master/miseval
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d6ab5bd34aeb19475c3f3123e10b6093 Zobrazit plný text záznamu