Using cell nuclei features to detect colon cancer tissue in hematoxylin and eosin stained slides
| Autor: | Simon Kragh Andersen, Rasmus Røge, Alex Skovsbo Jørgensen, Anders Munk Rasmussen, Niels Kristian Mäkinen Andersen, Lasse Riis Østergaard, Jonas Emborg |
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| Rok vydání: | 2017 |
| Předmět: |
Pathology
medicine.medical_specialty Histology Colorectal cancer Cell growth Cell H&E stain Cancer 02 engineering and technology Cell Biology Anatomy Biology medicine.disease Thresholding Pathology and Forensic Medicine 03 medical and health sciences 0302 clinical medicine medicine.anatomical_structure 030220 oncology & carcinogenesis 0202 electrical engineering electronic engineering information engineering Cell separation medicine 020201 artificial intelligence & image processing Cytometry |
| Zdroj: | Cytometry Part A. 91:785-793 |
| ISSN: | 1552-4922 |
| DOI: | 10.1002/cyto.a.23175 |
| Popis: | Currently, diagnosis of colon cancer is based on manual examination of histopathological images by a pathologist. This can be time consuming and interpretation of the images is subject to inter- and intra-observer variability. This may be improved by introducing a computer-aided diagnosis (CAD) system for automatic detection of cancer tissue within whole slide hematoxylin and eosin (H&E) stains. Cancer disrupts the normal control mechanisms of cell proliferation and differentiation, affecting the structure and appearance of the cells. Therefore, extracting features from segmented cell nuclei structures may provide useful information to detect cancer tissue. A framework for automatic classification of regions of interest (ROI) containing either benign or cancerous colon tissue extracted from whole slide H&E stained images using cell nuclei features was proposed. A total of 1,596 ROI's were extracted from 87 whole slide H&E stains (44 benign and 43 cancer). A cell nuclei segmentation algorithm consisting of color deconvolution, k-means clustering, local adaptive thresholding, and cell separation was performed within the ROI's to extract cell nuclei features. From the segmented cell nuclei structures a total of 750 texture and intensity-based features were extracted for classification of the ROI's. The nine most discriminative cell nuclei features were used in a random forest classifier to determine if the ROI's contained benign or cancer tissue. The ROI classification obtained an area under the curve (AUC) of 0.96, sensitivity of 0.88, specificity of 0.92, and accuracy of 0.91 using an optimized threshold. The developed framework showed promising results in using cell nuclei features to classify ROIs into containing benign or cancer tissue in H&E stained tissue samples. © 2017 International Society for Advancement of Cytometry. |
| Databáze: | OpenAIRE |
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