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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...

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Autores principales: Hartmann, Dennis, Schmid, Verena, Meyer, Philip, Auer, Florian, Soto-Rey, Iñaki, Müller, Dominik, Kramer, Frank
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10453729/
https://www.ncbi.nlm.nih.gov/pubmed/37627877
http://dx.doi.org/10.3390/diagnostics13162618
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author Hartmann, Dennis
Schmid, Verena
Meyer, Philip
Auer, Florian
Soto-Rey, Iñaki
Müller, Dominik
Kramer, Frank
author_facet Hartmann, Dennis
Schmid, Verena
Meyer, Philip
Auer, Florian
Soto-Rey, Iñaki
Müller, Dominik
Kramer, Frank
author_sort Hartmann, Dennis
collection PubMed
description 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.
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spelling pubmed-104537292023-08-26 MISM: A Medical Image Segmentation Metric for Evaluation of Weak Labeled Data Hartmann, Dennis Schmid, Verena Meyer, Philip Auer, Florian Soto-Rey, Iñaki Müller, Dominik Kramer, Frank Diagnostics (Basel) Brief Report 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. MDPI 2023-08-08 /pmc/articles/PMC10453729/ /pubmed/37627877 http://dx.doi.org/10.3390/diagnostics13162618 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Brief Report
Hartmann, Dennis
Schmid, Verena
Meyer, Philip
Auer, Florian
Soto-Rey, Iñaki
Müller, Dominik
Kramer, Frank
MISM: A Medical Image Segmentation Metric for Evaluation of Weak Labeled Data
title MISM: A Medical Image Segmentation Metric for Evaluation of Weak Labeled Data
title_full MISM: A Medical Image Segmentation Metric for Evaluation of Weak Labeled Data
title_fullStr MISM: A Medical Image Segmentation Metric for Evaluation of Weak Labeled Data
title_full_unstemmed MISM: A Medical Image Segmentation Metric for Evaluation of Weak Labeled Data
title_short MISM: A Medical Image Segmentation Metric for Evaluation of Weak Labeled Data
title_sort mism: a medical image segmentation metric for evaluation of weak labeled data
topic Brief Report
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10453729/
https://www.ncbi.nlm.nih.gov/pubmed/37627877
http://dx.doi.org/10.3390/diagnostics13162618
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