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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2015-01-01
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| Series: | Journal of Pathology Informatics |
| Subjects: | |
| Online Access: | http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2015;volume=6;issue=1;spage=15;epage=15;aulast=Mousavi |
| _version_ | 1811244658780536832 |
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| author | Hojjat Seyed Mousavi Vishal Monga Ganesh Rao Arvind U. K. Rao |
| author_facet | Hojjat Seyed Mousavi Vishal Monga Ganesh Rao Arvind U. K. Rao |
| author_sort | Hojjat Seyed Mousavi |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-12T14:29:05Z |
| format | Article |
| id | doaj.art-c4e27003ee7e4c4990dc469267412024 |
| institution | Directory Open Access Journal |
| issn | 2153-3539 |
| language | English |
| last_indexed | 2024-04-12T14:29:05Z |
| publishDate | 2015-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Pathology Informatics |
| spelling | doaj.art-c4e27003ee7e4c4990dc4692674120242022-12-22T03:29:21ZengElsevierJournal of Pathology Informatics2153-35392015-01-0161151510.4103/2153-3539.153914Automated discrimination of lower and higher grade gliomas based on histopathological image analysisHojjat Seyed MousaviVishal MongaGanesh RaoArvind U. K. RaoIntroduction: 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.http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2015;volume=6;issue=1;spage=15;epage=15;aulast=MousaviBrain cancer, cell segmentation, computer-aided diagnosis, decision tree, glioblastoma multiforme, low-grade glioma, micro-vascular proliferation, morphological transformation, pathological grading, pseudopalisading necrosis, spatial analysis, spatial filters |
| spellingShingle | Hojjat Seyed Mousavi Vishal Monga Ganesh Rao Arvind U. K. Rao Automated discrimination of lower and higher grade gliomas based on histopathological image analysis Journal of Pathology Informatics Brain cancer, cell segmentation, computer-aided diagnosis, decision tree, glioblastoma multiforme, low-grade glioma, micro-vascular proliferation, morphological transformation, pathological grading, pseudopalisading necrosis, spatial analysis, spatial filters |
| 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 | Brain cancer, cell segmentation, computer-aided diagnosis, decision tree, glioblastoma multiforme, low-grade glioma, micro-vascular proliferation, morphological transformation, pathological grading, pseudopalisading necrosis, spatial analysis, spatial filters |
| url | http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2015;volume=6;issue=1;spage=15;epage=15;aulast=Mousavi |
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