Automated detection of DCIS in whole-slide H E stained breast histopathology images / Babak Ehteshami Bejnordi, Maschenka Balkenhol, Geert Litjens, Roland Holland, Peter Bult, Nico Karssemeijer, Jeroen A. W. M. van der Laak
This paper presents and evaluates a fully automatic method for detection of ductal carcinoma in situ (DCIS) in digitized hematoxylin and eosin (H&E) stained histopathological slides of breast tissue. The proposed method applies multi-scale superpixel classification to detect epithelial regions in whole-slide images (WSIs). Subsequently, spatial clustering is utilized to delineate regions representing meaningful structures within the tissue such as ducts and lobules. A region-based classifier employing a large set of features including statistical and structural texture features and architectural features is then trained to discriminate between DCIS and benign/normal structures. The system is evaluated on two datasets containing a total of 205 WSIs of breast tissue. Evaluation was conducted both on the slide and the lesion level using FROC analysis. The results show that to detect at least one true positive in every DCIS containing slide, the system finds 2.6 false positives per WSI. The results of the per-lesion evaluation show that it is possible to detect 80% and 83% of the DCIS lesions in an abnormal slide, at an average of 2.0 and 3.0 false positives per WSI, respectively. Collectively, the result of the experiments demonstrate the efficacy and accuracy of the proposed method as well as its potential for application in routine pathological diagnostics. To the best of our knowledge, this is the first DCIS detection algorithm working fully automatically on WSIs..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
05 April 2016 2016 |
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| Erschienen: |
05 April 2016 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:35 |
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| Enthalten in: |
IEEE transactions on medical imaging - 35(2016), 9, Seite 2141-2150 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Bejnordi, Babak Ehteshami, 1986- [VerfasserIn] |
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| Links: |
Volltext [lizenzpflichtig] |
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| Anmerkungen: |
Gesehen am 05.05.2020 |
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| Umfang: |
10 |
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| doi: |
10.1109/TMI.2016.2550620 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 245 | 1 | 0 | |a Automated detection of DCIS in whole-slide H E stained breast histopathology images |c Babak Ehteshami Bejnordi, Maschenka Balkenhol, Geert Litjens, Roland Holland, Peter Bult, Nico Karssemeijer, Jeroen A. W. M. van der Laak |
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| 520 | |a This paper presents and evaluates a fully automatic method for detection of ductal carcinoma in situ (DCIS) in digitized hematoxylin and eosin (H&E) stained histopathological slides of breast tissue. The proposed method applies multi-scale superpixel classification to detect epithelial regions in whole-slide images (WSIs). Subsequently, spatial clustering is utilized to delineate regions representing meaningful structures within the tissue such as ducts and lobules. A region-based classifier employing a large set of features including statistical and structural texture features and architectural features is then trained to discriminate between DCIS and benign/normal structures. The system is evaluated on two datasets containing a total of 205 WSIs of breast tissue. Evaluation was conducted both on the slide and the lesion level using FROC analysis. The results show that to detect at least one true positive in every DCIS containing slide, the system finds 2.6 false positives per WSI. The results of the per-lesion evaluation show that it is possible to detect 80% and 83% of the DCIS lesions in an abnormal slide, at an average of 2.0 and 3.0 false positives per WSI, respectively. Collectively, the result of the experiments demonstrate the efficacy and accuracy of the proposed method as well as its potential for application in routine pathological diagnostics. To the best of our knowledge, this is the first DCIS detection algorithm working fully automatically on WSIs. | ||
| 650 | 4 | |a Algorithms | |
| 650 | 4 | |a architectural features | |
| 650 | 4 | |a benign structures | |
| 650 | 4 | |a biomedical optical imaging | |
| 650 | 4 | |a Breast | |
| 650 | 4 | |a Breast Neoplasms | |
| 650 | 4 | |a breast tissue | |
| 650 | 4 | |a Breast tissue | |
| 650 | 4 | |a cancer | |
| 650 | 4 | |a Cancer | |
| 650 | 4 | |a Carcinoma, Ductal, Breast | |
| 650 | 4 | |a Carcinoma, Intraductal, Noninfiltrating | |
| 650 | 4 | |a Clustering algorithms | |
| 650 | 4 | |a Computer-aided diagnosis | |
| 650 | 4 | |a DCIS Detection | |
| 650 | 4 | |a DCIS detection algorithm | |
| 650 | 4 | |a DCIS lesion | |
| 650 | 4 | |a Design automation | |
| 650 | 4 | |a digitized hematoxylin and eosin stained histopathological slides | |
| 650 | 4 | |a ductal carcinoma in situ detection | |
| 650 | 4 | |a ducts | |
| 650 | 4 | |a epithelial region | |
| 650 | 4 | |a Feature extraction | |
| 650 | 4 | |a FROC analysis | |
| 650 | 4 | |a fully automatic method | |
| 650 | 4 | |a H&E staining | |
| 650 | 4 | |a Humans | |
| 650 | 4 | |a image classification | |
| 650 | 4 | |a image texture | |
| 650 | 4 | |a lesion level | |
| 650 | 4 | |a Lesions | |
| 650 | 4 | |a lobules | |
| 650 | 4 | |a meaningful structures | |
| 650 | 4 | |a medical image processing | |
| 650 | 4 | |a multiscale superpixel classification | |
| 650 | 4 | |a normal structures | |
| 650 | 4 | |a Pathology | |
| 650 | 4 | |a region-based classifier | |
| 650 | 4 | |a routine pathological diagnostics | |
| 650 | 4 | |a spatial clustering | |
| 650 | 4 | |a statistical texture features | |
| 650 | 4 | |a structural texture features | |
| 650 | 4 | |a tumours | |
| 650 | 4 | |a whole-slide H&E stained breast histopathology images | |
| 650 | 4 | |a whole-slide imaging | |
| 650 | 4 | |a WSI | |
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