Time-efficient sparse analysis of histopathological whole slide images

Abstract

Histopathological examination is a powerful method for the prognosis of critical diseases. But, despite significant advances in high-speed and high-resolution scanning devices or in virtual exploration capabilities, the clinical analysis of Whole Slide Images (WSI) largely remains the work of human experts. We propose an innovative platform in which multi-scale computer vision algorithms perform fast analysis of a histopathological WSI. It relies on specific high and generic low resolution image analysis algorithms embedded in a multi-scale framework to rapidly identify the high power fields of interest used by the pathologist to assess a global grading. GPU technologies as well speed up the global time-efficiency of the system. In a sense, sparse coding and sampling is the keystone of our approach. In terms of validation, we are designing a computer-aided breast biopsy analysis application based on histopathology images and designed in collaboration with a pathology department. The current ground truth slides correspond to about 36,000 high magnification (40X) high power fields. The time processing to achieve automatic WSI analysis is on a par with the pathologist's performance (about ten minutes a WSI), which constitutes by itself a major contribution of the proposed methodology.

Technologie des lames virtuelles

“…Enfin, la visualisation peut être faite en se connectant à une application web avec un navigateur internet usuel. Il y a trois priorités pour les logiciels de visualisation : la qualité des images, la rapidité de leur affichage [4] Enfin, ces fonctionnalités peuvent être complétées par des logiciels d'aide au diagnostic grâce à des outils d'analyse d'image [9,10], tels que le comptage automatique des noyaux de cellules présents sur la lame ou la mesure des surfaces de tissus.…”

Computer-aided prognosis on breast cancer with hematoxylin and eosin histopathology images: A review

“…39 Processing or analyzing WSIs manually is labor-intensive and has difficulties in capturing biologically relevant features from mass information. 40 Thus, there are compelling reasons to find automated, efficient, and high-throughput ways to analyze digital pathological images. 41 Image analysis can quantify pathological features on H&E histopathology images after individual cell nuclei and tissue structures are detected and segmented.…”

Automated Histology Analysis: Opportunities for signal processing

“…[36] uses a low-resolution input to extract features based on color and sparse coding of subpatches. These features are classified via support vector machine (SVM) to detect ROIs.…”