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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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. |
format | Online Article Text |
id | pubmed-10453729 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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