MISM: a medical image segmentation metric for evaluation of weak labeled data

  • 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 arise when images with a very small region of interest or without a region of interest at all are assessed. As a solution to these limitations, we propose a new medical image segmentation metric: MISm. This metric is a composition of the Dice similarity coefficient and the weighted specificity. MISm was investigated for definition gaps, an appropriate scoring gradient, and different weighting coefficients used to propose a constant value. Furthermore, an evaluation was performed by comparing the popular metrics in the medical image segmentation and MISm using images of magnet resonance tomography from several fictitious prediction scenarios. Our analysis shows that MISm can be applied in a general way and thus also covers the mentioned edge cases,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 arise when images with a very small region of interest or without a region of interest at all are assessed. As a solution to these limitations, we propose a new medical image segmentation metric: MISm. This metric is a composition of the Dice similarity coefficient and the weighted specificity. MISm was investigated for definition gaps, an appropriate scoring gradient, and different weighting coefficients used to propose a constant value. Furthermore, an evaluation was performed by comparing the popular metrics in the medical image segmentation and MISm using images of magnet resonance tomography from several fictitious prediction scenarios. Our analysis shows that MISm can be applied in a general way and thus also covers the mentioned edge cases, which are not covered by other metrics, in a reasonable way. In order to allow easy access to MISm and therefore widespread application in the community, as well as reproducibility of experimental results, we included MISm in the publicly available evaluation framework MISeval.show moreshow less

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Metadaten
Author:Dennis Hartmann, Verena Schmid, Philip Meyer, Florian AuerORCiDGND, Iñaki Soto-ReyORCiD, Dominik MüllerORCiDGND, Frank KramerORCiDGND
URN:urn:nbn:de:bvb:384-opus4-1082645
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/108264
ISSN:2075-4418OPAC
Parent Title (English):Diagnostics
Publisher:MDPI AG
Type:Article
Language:English
Date of first Publication:2023/08/08
Publishing Institution:Universität Augsburg
Release Date:2023/10/11
Tag:Clinical Biochemistry
Volume:13
Issue:16
First Page:2618
DOI:https://doi.org/10.3390/diagnostics13162618
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Medizinische Fakultät
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für IT-Infrastrukturen für die Translationale Medizinische Forschung
Medizinische Fakultät / Universitätsklinikum
Medizinische Fakultät / Lehrstuhl für Datenmanagement und Clinical Decision Support
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Licence (German):CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand)