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Determining Image Processing Features Describing the Appearance of Challenging Mitotic Figures and Miscounted Nonmitotic Objects

CONTEXT: Previous studies showed that the agreement among pathologists in recognition of mitoses in breast slides is fairly modest. AIMS: Determining the significantly different quantitative features among easily identifiable mitoses, challenging mitoses, and miscounted nonmitoses within breast slid...

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Autores principales: Gandomkar, Ziba, Brennan, Patrick C., Mello-Thoms, Claudia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Medknow Publications & Media Pvt Ltd 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5609395/
https://www.ncbi.nlm.nih.gov/pubmed/28966834
http://dx.doi.org/10.4103/jpi.jpi_22_17
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author Gandomkar, Ziba
Brennan, Patrick C.
Mello-Thoms, Claudia
author_facet Gandomkar, Ziba
Brennan, Patrick C.
Mello-Thoms, Claudia
author_sort Gandomkar, Ziba
collection PubMed
description CONTEXT: Previous studies showed that the agreement among pathologists in recognition of mitoses in breast slides is fairly modest. AIMS: Determining the significantly different quantitative features among easily identifiable mitoses, challenging mitoses, and miscounted nonmitoses within breast slides and identifying which color spaces capture the difference among groups better than others. MATERIALS AND METHODS: The dataset contained 453 mitoses and 265 miscounted objects in breast slides. The mitoses were grouped into three categories based on the confidence degree of three pathologists who annotated them. The mitoses annotated as “probably a mitosis” by the majority of pathologists were considered as the challenging category. The miscounted objects were recognized as a mitosis or probably a mitosis by only one of the pathologists. The mitoses were segmented using k-means clustering, followed by morphological operations. Morphological, intensity-based, and textural features were extracted from the segmented area and also the image patch of 63 × 63 pixels in different channels of eight color spaces. Holistic features describing the mitoses' surrounding cells of each image were also extracted. STATISTICAL ANALYSIS USED: The Kruskal–Wallis H-test followed by the Tukey-Kramer test was used to identify significantly different features. RESULTS: The results indicated that challenging mitoses were smaller and rounder compared to other mitoses. Among different features, the Gabor textural features differed more than others between challenging mitoses and the easily identifiable ones. Sizes of the non-mitoses were similar to easily identifiable mitoses, but nonmitoses were rounder. The intensity-based features from chromatin channels were the most discriminative features between the easily identifiable mitoses and the miscounted objects. CONCLUSIONS: Quantitative features can be used to describe the characteristics of challenging mitoses and miscounted nonmitotic objects.
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spelling pubmed-56093952017-09-29 Determining Image Processing Features Describing the Appearance of Challenging Mitotic Figures and Miscounted Nonmitotic Objects Gandomkar, Ziba Brennan, Patrick C. Mello-Thoms, Claudia J Pathol Inform Original Article CONTEXT: Previous studies showed that the agreement among pathologists in recognition of mitoses in breast slides is fairly modest. AIMS: Determining the significantly different quantitative features among easily identifiable mitoses, challenging mitoses, and miscounted nonmitoses within breast slides and identifying which color spaces capture the difference among groups better than others. MATERIALS AND METHODS: The dataset contained 453 mitoses and 265 miscounted objects in breast slides. The mitoses were grouped into three categories based on the confidence degree of three pathologists who annotated them. The mitoses annotated as “probably a mitosis” by the majority of pathologists were considered as the challenging category. The miscounted objects were recognized as a mitosis or probably a mitosis by only one of the pathologists. The mitoses were segmented using k-means clustering, followed by morphological operations. Morphological, intensity-based, and textural features were extracted from the segmented area and also the image patch of 63 × 63 pixels in different channels of eight color spaces. Holistic features describing the mitoses' surrounding cells of each image were also extracted. STATISTICAL ANALYSIS USED: The Kruskal–Wallis H-test followed by the Tukey-Kramer test was used to identify significantly different features. RESULTS: The results indicated that challenging mitoses were smaller and rounder compared to other mitoses. Among different features, the Gabor textural features differed more than others between challenging mitoses and the easily identifiable ones. Sizes of the non-mitoses were similar to easily identifiable mitoses, but nonmitoses were rounder. The intensity-based features from chromatin channels were the most discriminative features between the easily identifiable mitoses and the miscounted objects. CONCLUSIONS: Quantitative features can be used to describe the characteristics of challenging mitoses and miscounted nonmitotic objects. Medknow Publications & Media Pvt Ltd 2017-09-07 /pmc/articles/PMC5609395/ /pubmed/28966834 http://dx.doi.org/10.4103/jpi.jpi_22_17 Text en Copyright: © 2017 Journal of Pathology Informatics http://creativecommons.org/licenses/by-nc-sa/3.0 This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms.
spellingShingle Original Article
Gandomkar, Ziba
Brennan, Patrick C.
Mello-Thoms, Claudia
Determining Image Processing Features Describing the Appearance of Challenging Mitotic Figures and Miscounted Nonmitotic Objects
title Determining Image Processing Features Describing the Appearance of Challenging Mitotic Figures and Miscounted Nonmitotic Objects
title_full Determining Image Processing Features Describing the Appearance of Challenging Mitotic Figures and Miscounted Nonmitotic Objects
title_fullStr Determining Image Processing Features Describing the Appearance of Challenging Mitotic Figures and Miscounted Nonmitotic Objects
title_full_unstemmed Determining Image Processing Features Describing the Appearance of Challenging Mitotic Figures and Miscounted Nonmitotic Objects
title_short Determining Image Processing Features Describing the Appearance of Challenging Mitotic Figures and Miscounted Nonmitotic Objects
title_sort determining image processing features describing the appearance of challenging mitotic figures and miscounted nonmitotic objects
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5609395/
https://www.ncbi.nlm.nih.gov/pubmed/28966834
http://dx.doi.org/10.4103/jpi.jpi_22_17
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