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Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool
BACKGROUND: Medical Image segmentation is an important image processing step. Comparing images to evaluate the quality of segmentation is an essential part of measuring progress in this research area. Some of the challenges in evaluating medical segmentation are: metric selection, the use in the lit...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4533825/ https://www.ncbi.nlm.nih.gov/pubmed/26263899 http://dx.doi.org/10.1186/s12880-015-0068-x |
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author | Taha, Abdel Aziz Hanbury, Allan |
author_facet | Taha, Abdel Aziz Hanbury, Allan |
author_sort | Taha, Abdel Aziz |
collection | PubMed |
description | BACKGROUND: Medical Image segmentation is an important image processing step. Comparing images to evaluate the quality of segmentation is an essential part of measuring progress in this research area. Some of the challenges in evaluating medical segmentation are: metric selection, the use in the literature of multiple definitions for certain metrics, inefficiency of the metric calculation implementations leading to difficulties with large volumes, and lack of support for fuzzy segmentation by existing metrics. RESULT: First we present an overview of 20 evaluation metrics selected based on a comprehensive literature review. For fuzzy segmentation, which shows the level of membership of each voxel to multiple classes, fuzzy definitions of all metrics are provided. We present a discussion about metric properties to provide a guide for selecting evaluation metrics. Finally, we propose an efficient evaluation tool implementing the 20 selected metrics. The tool is optimized to perform efficiently in terms of speed and required memory, also if the image size is extremely large as in the case of whole body MRI or CT volume segmentation. An implementation of this tool is available as an open source project. CONCLUSION: We propose an efficient evaluation tool for 3D medical image segmentation using 20 evaluation metrics and provide guidelines for selecting a subset of these metrics that is suitable for the data and the segmentation task. |
format | Online Article Text |
id | pubmed-4533825 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-45338252015-08-13 Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool Taha, Abdel Aziz Hanbury, Allan BMC Med Imaging Software BACKGROUND: Medical Image segmentation is an important image processing step. Comparing images to evaluate the quality of segmentation is an essential part of measuring progress in this research area. Some of the challenges in evaluating medical segmentation are: metric selection, the use in the literature of multiple definitions for certain metrics, inefficiency of the metric calculation implementations leading to difficulties with large volumes, and lack of support for fuzzy segmentation by existing metrics. RESULT: First we present an overview of 20 evaluation metrics selected based on a comprehensive literature review. For fuzzy segmentation, which shows the level of membership of each voxel to multiple classes, fuzzy definitions of all metrics are provided. We present a discussion about metric properties to provide a guide for selecting evaluation metrics. Finally, we propose an efficient evaluation tool implementing the 20 selected metrics. The tool is optimized to perform efficiently in terms of speed and required memory, also if the image size is extremely large as in the case of whole body MRI or CT volume segmentation. An implementation of this tool is available as an open source project. CONCLUSION: We propose an efficient evaluation tool for 3D medical image segmentation using 20 evaluation metrics and provide guidelines for selecting a subset of these metrics that is suitable for the data and the segmentation task. BioMed Central 2015-08-12 /pmc/articles/PMC4533825/ /pubmed/26263899 http://dx.doi.org/10.1186/s12880-015-0068-x Text en © Taha and Hanbury. 2015 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Software Taha, Abdel Aziz Hanbury, Allan Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool |
title | Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool |
title_full | Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool |
title_fullStr | Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool |
title_full_unstemmed | Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool |
title_short | Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool |
title_sort | metrics for evaluating 3d medical image segmentation: analysis, selection, and tool |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4533825/ https://www.ncbi.nlm.nih.gov/pubmed/26263899 http://dx.doi.org/10.1186/s12880-015-0068-x |
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