Mitotic Figure Recognition: Agreement among Pathologists and Computerized Detector
Despite the prognostic importance of mitotic count as one of the components of the Bloom – Richardson grade [3], several studies ([2, 9, 10]) have found that pathologists’ agreement on the mitotic grade is fairly modest. Collecting a set of more than 4,200 candidate mitotic figures, we evaluate path...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2012-01-01
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| Series: | Analytical Cellular Pathology |
| Online Access: | http://dx.doi.org/10.3233/ACP-2011-0029 |
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| _version_ | 1850168569790201856 |
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| author | Christopher Malon Elena Brachtel Eric Cosatto Hans Peter Graf Atsushi Kurata Masahiko Kuroda John S. Meyer Akira Saito Shulin Wu Yukako Yagi |
| author_facet | Christopher Malon Elena Brachtel Eric Cosatto Hans Peter Graf Atsushi Kurata Masahiko Kuroda John S. Meyer Akira Saito Shulin Wu Yukako Yagi |
| author_sort | Christopher Malon |
| collection | DOAJ |
| description | Despite the prognostic importance of mitotic count as one of the components of the Bloom – Richardson grade [3], several studies ([2, 9, 10]) have found that pathologists’ agreement on the mitotic grade is fairly modest. Collecting a set of more than 4,200 candidate mitotic figures, we evaluate pathologists' agreement on individual figures, and train a computerized system for mitosis detection, comparing its performance to the classifications of three pathologists. The system’s and the pathologists’ classifications are based on evaluation of digital micrographs of hematoxylin and eosin stained breast tissue. On figures where the majority of pathologists agree on a classification, we compare the performance of the trained system to that of the individual pathologists. We find that the level of agreement of the pathologists ranges from slight to moderate, with strong biases, and that the system performs competitively in rating the ground truth set. This study is a step towards automatic mitosis count to accelerate a pathologist's work and improve reproducibility. |
| format | Article |
| id | doaj-art-a779ca306ffb4affb30d9a1834e11fa9 |
| institution | OA Journals |
| issn | 2210-7177 2210-7185 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Analytical Cellular Pathology |
| spelling | doaj-art-a779ca306ffb4affb30d9a1834e11fa92025-08-20T02:20:56ZengWileyAnalytical Cellular Pathology2210-71772210-71852012-01-013529710010.3233/ACP-2011-0029Mitotic Figure Recognition: Agreement among Pathologists and Computerized DetectorChristopher Malon0Elena Brachtel1Eric Cosatto2Hans Peter Graf3Atsushi Kurata4Masahiko Kuroda5John S. Meyer6Akira Saito7Shulin Wu8Yukako Yagi9Department of Machine Learning, NEC Laboratories America, NJ, USADepartment of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USADepartment of Machine Learning, NEC Laboratories America, NJ, USADepartment of Machine Learning, NEC Laboratories America, NJ, USADepartment of Molecular Pathology, Tokyo Medical University, Tokyo, JapanDepartment of Molecular Pathology, Tokyo Medical University, Tokyo, JapanDepartment of Pathology, St. Luke's Hospital (St. Louis), Chesterfield, MO, USAInnovative Service Solutions Division, NEC Corporation, Tokyo, JapanDepartment of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USADepartment of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USADespite the prognostic importance of mitotic count as one of the components of the Bloom – Richardson grade [3], several studies ([2, 9, 10]) have found that pathologists’ agreement on the mitotic grade is fairly modest. Collecting a set of more than 4,200 candidate mitotic figures, we evaluate pathologists' agreement on individual figures, and train a computerized system for mitosis detection, comparing its performance to the classifications of three pathologists. The system’s and the pathologists’ classifications are based on evaluation of digital micrographs of hematoxylin and eosin stained breast tissue. On figures where the majority of pathologists agree on a classification, we compare the performance of the trained system to that of the individual pathologists. We find that the level of agreement of the pathologists ranges from slight to moderate, with strong biases, and that the system performs competitively in rating the ground truth set. This study is a step towards automatic mitosis count to accelerate a pathologist's work and improve reproducibility.http://dx.doi.org/10.3233/ACP-2011-0029 |
| spellingShingle | Christopher Malon Elena Brachtel Eric Cosatto Hans Peter Graf Atsushi Kurata Masahiko Kuroda John S. Meyer Akira Saito Shulin Wu Yukako Yagi Mitotic Figure Recognition: Agreement among Pathologists and Computerized Detector Analytical Cellular Pathology |
| title | Mitotic Figure Recognition: Agreement among Pathologists and Computerized Detector |
| title_full | Mitotic Figure Recognition: Agreement among Pathologists and Computerized Detector |
| title_fullStr | Mitotic Figure Recognition: Agreement among Pathologists and Computerized Detector |
| title_full_unstemmed | Mitotic Figure Recognition: Agreement among Pathologists and Computerized Detector |
| title_short | Mitotic Figure Recognition: Agreement among Pathologists and Computerized Detector |
| title_sort | mitotic figure recognition agreement among pathologists and computerized detector |
| url | http://dx.doi.org/10.3233/ACP-2011-0029 |
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