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Automated discrimination of lower and higher grade gliomas based on histopathological image analysis
INTRODUCTION: Histopathological images have rich structural information, are multi-channel in nature and contain meaningful pathological information at various scales. Sophisticated image analysis tools that can automatically extract discriminative information from the histopathology image slides fo...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Medknow Publications & Media Pvt Ltd
2015
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4382761/ https://www.ncbi.nlm.nih.gov/pubmed/25838967 http://dx.doi.org/10.4103/2153-3539.153914 |
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author | Mousavi, Hojjat Seyed Monga, Vishal Rao, Ganesh Rao, Arvind U. K. |
author_facet | Mousavi, Hojjat Seyed Monga, Vishal Rao, Ganesh Rao, Arvind U. K. |
author_sort | Mousavi, Hojjat Seyed |
collection | PubMed |
description | INTRODUCTION: Histopathological images have rich structural information, are multi-channel in nature and contain meaningful pathological information at various scales. Sophisticated image analysis tools that can automatically extract discriminative information from the histopathology image slides for diagnosis remain an area of significant research activity. In this work, we focus on automated brain cancer grading, specifically glioma grading. Grading of a glioma is a highly important problem in pathology and is largely done manually by medical experts based on an examination of pathology slides (images). To complement the efforts of clinicians engaged in brain cancer diagnosis, we develop novel image processing algorithms and systems to automatically grade glioma tumor into two categories: Low-grade glioma (LGG) and high-grade glioma (HGG) which represent a more advanced stage of the disease. RESULTS: We propose novel image processing algorithms based on spatial domain analysis for glioma tumor grading that will complement the clinical interpretation of the tissue. The image processing techniques are developed in close collaboration with medical experts to mimic the visual cues that a clinician looks for in judging of the grade of the disease. Specifically, two algorithmic techniques are developed: (1) A cell segmentation and cell-count profile creation for identification of Pseudopalisading Necrosis, and (2) a customized operation of spatial and morphological filters to accurately identify microvascular proliferation (MVP). In both techniques, a hierarchical decision is made via a decision tree mechanism. If either Pseudopalisading Necrosis or MVP is found present in any part of the histopathology slide, the whole slide is identified as HGG, which is consistent with World Health Organization guidelines. Experimental results on the Cancer Genome Atlas database are presented in the form of: (1) Successful detection rates of pseudopalisading necrosis and MVP regions, (2) overall classification accuracy into LGG and HGG categories, and (3) receiver operating characteristic curves which can facilitate a desirable trade-off between HGG detection and false-alarm rates. CONCLUSION: The proposed method demonstrates fairly high accuracy and compares favorably against best-known alternatives such as the state-of-the-art WND-CHARM feature set provided by NIH combined with powerful support vector machine classifier. Our results reveal that the proposed method can be beneficial to a clinician in effectively separating histopathology slides into LGG and HGG categories, particularly where the analysis of a large number of slides is needed. Our work also reveals that MVP regions are much harder to detect than Pseudopalisading Necrosis and increasing accuracy of automated image processing for MVP detection emerges as a significant future research direction. |
format | Online Article Text |
id | pubmed-4382761 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Medknow Publications & Media Pvt Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-43827612015-04-02 Automated discrimination of lower and higher grade gliomas based on histopathological image analysis Mousavi, Hojjat Seyed Monga, Vishal Rao, Ganesh Rao, Arvind U. K. J Pathol Inform Research Article INTRODUCTION: Histopathological images have rich structural information, are multi-channel in nature and contain meaningful pathological information at various scales. Sophisticated image analysis tools that can automatically extract discriminative information from the histopathology image slides for diagnosis remain an area of significant research activity. In this work, we focus on automated brain cancer grading, specifically glioma grading. Grading of a glioma is a highly important problem in pathology and is largely done manually by medical experts based on an examination of pathology slides (images). To complement the efforts of clinicians engaged in brain cancer diagnosis, we develop novel image processing algorithms and systems to automatically grade glioma tumor into two categories: Low-grade glioma (LGG) and high-grade glioma (HGG) which represent a more advanced stage of the disease. RESULTS: We propose novel image processing algorithms based on spatial domain analysis for glioma tumor grading that will complement the clinical interpretation of the tissue. The image processing techniques are developed in close collaboration with medical experts to mimic the visual cues that a clinician looks for in judging of the grade of the disease. Specifically, two algorithmic techniques are developed: (1) A cell segmentation and cell-count profile creation for identification of Pseudopalisading Necrosis, and (2) a customized operation of spatial and morphological filters to accurately identify microvascular proliferation (MVP). In both techniques, a hierarchical decision is made via a decision tree mechanism. If either Pseudopalisading Necrosis or MVP is found present in any part of the histopathology slide, the whole slide is identified as HGG, which is consistent with World Health Organization guidelines. Experimental results on the Cancer Genome Atlas database are presented in the form of: (1) Successful detection rates of pseudopalisading necrosis and MVP regions, (2) overall classification accuracy into LGG and HGG categories, and (3) receiver operating characteristic curves which can facilitate a desirable trade-off between HGG detection and false-alarm rates. CONCLUSION: The proposed method demonstrates fairly high accuracy and compares favorably against best-known alternatives such as the state-of-the-art WND-CHARM feature set provided by NIH combined with powerful support vector machine classifier. Our results reveal that the proposed method can be beneficial to a clinician in effectively separating histopathology slides into LGG and HGG categories, particularly where the analysis of a large number of slides is needed. Our work also reveals that MVP regions are much harder to detect than Pseudopalisading Necrosis and increasing accuracy of automated image processing for MVP detection emerges as a significant future research direction. Medknow Publications & Media Pvt Ltd 2015-03-24 /pmc/articles/PMC4382761/ /pubmed/25838967 http://dx.doi.org/10.4103/2153-3539.153914 Text en Copyright: © 2015 Mousavi HS. http://creativecommons.org/licenses/by-nc-sa/3.0 This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Mousavi, Hojjat Seyed Monga, Vishal Rao, Ganesh Rao, Arvind U. K. Automated discrimination of lower and higher grade gliomas based on histopathological image analysis |
title | Automated discrimination of lower and higher grade gliomas based on histopathological image analysis |
title_full | Automated discrimination of lower and higher grade gliomas based on histopathological image analysis |
title_fullStr | Automated discrimination of lower and higher grade gliomas based on histopathological image analysis |
title_full_unstemmed | Automated discrimination of lower and higher grade gliomas based on histopathological image analysis |
title_short | Automated discrimination of lower and higher grade gliomas based on histopathological image analysis |
title_sort | automated discrimination of lower and higher grade gliomas based on histopathological image analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4382761/ https://www.ncbi.nlm.nih.gov/pubmed/25838967 http://dx.doi.org/10.4103/2153-3539.153914 |
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