Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles.

Détails

ID Serval
serval:BIB_39877F613382
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles.
Périodique
Medical image analysis
Auteur⸱e⸱s
Barker J., Hoogi A., Depeursinge A., Rubin D.L.
ISSN
1361-8423 (Electronic)
ISSN-L
1361-8415
Statut éditorial
Publié
Date de publication
05/2016
Peer-reviewed
Oui
Volume
30
Pages
60-71
Langue
anglais
Notes
Publication types: Evaluation Study ; Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Résumé
Computerized analysis of digital pathology images offers the potential of improving clinical care (e.g. automated diagnosis) and catalyzing research (e.g. discovering disease subtypes). There are two key challenges thwarting computerized analysis of digital pathology images: first, whole slide pathology images are massive, making computerized analysis inefficient, and second, diverse tissue regions in whole slide images that are not directly relevant to the disease may mislead computerized diagnosis algorithms. We propose a method to overcome both of these challenges that utilizes a coarse-to-fine analysis of the localized characteristics in pathology images. An initial surveying stage analyzes the diversity of coarse regions in the whole slide image. This includes extraction of spatially localized features of shape, color and texture from tiled regions covering the slide. Dimensionality reduction of the features assesses the image diversity in the tiled regions and clustering creates representative groups. A second stage provides a detailed analysis of a single representative tile from each group. An Elastic Net classifier produces a diagnostic decision value for each representative tile. A weighted voting scheme aggregates the decision values from these tiles to obtain a diagnosis at the whole slide level. We evaluated our method by automatically classifying 302 brain cancer cases into two possible diagnoses (glioblastoma multiforme (N = 182) versus lower grade glioma (N = 120)) with an accuracy of 93.1% (p << 0.001). We also evaluated our method in the dataset provided for the 2014 MICCAI Pathology Classification Challenge, in which our method, trained and tested using 5-fold cross validation, produced a classification accuracy of 100% (p << 0.001). Our method showed high stability and robustness to parameter variation, with accuracy varying between 95.5% and 100% when evaluated for a wide range of parameters. Our approach may be useful to automatically differentiate between the two cancer subtypes.
Mots-clé
Algorithms, Biopsy/methods, Brain Neoplasms/diagnostic imaging, Brain Neoplasms/pathology, Diagnosis, Differential, Glioma/classification, Glioma/diagnostic imaging, Glioma/pathology, Humans, Image Enhancement/methods, Image Interpretation, Computer-Assisted/methods, Microscopy/methods, Pathology/methods, Pattern Recognition, Automated/methods, Reproducibility of Results, Sensitivity and Specificity, Signal Processing, Computer-Assisted, Subtraction Technique, Computer aided diagnosis, Digital pathology, Object classification
Pubmed
Web of science
Open Access
Oui
Création de la notice
29/08/2023 8:44
Dernière modification de la notice
09/10/2023 15:50
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