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Proceedings Paper

Fast GPU-based segmentation of H&E stained squamous epithelium from multi-gigapixel tiled virtual slides
Author(s): Benjamin Bryant; Hamed Sari-Sarraf; Mitchell Wachtel; Rodney Long; Sameer Antani
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Paper Abstract

The processing of multi-gigapixel virtual histology slides presents a computationally intensive and time consuming task. Common tiled TIFF slide formats, such as those used by Aperio [1], contain inherent header information that can be used to rapidly locate tissue regions for cervical intraepithelial neoplasia (CIN) diagnosis. Tiles used in these formats are individually compressed subsections of the virtual slide, whose compression ratio varies based on their individual content. This paper discusses a method that exploits this information to rapidly identify regions of interest in an iterative process to locate epithelial tissue. These regions are decompressed using a multi-core CPU, from which a Compute Unified Device Architecture (CUDA) enabled GPU rapidly generates features and Support Vector Machine (SVM) decisions. SVM classifier results are used in a post-processing scheme to remove apparently spurious misclassifications. The mean overall execution time when using a high-end desktop PC, together with a GTX 560 GPU, is roughly 3 seconds per gigapixel, while maintaining the area under an ROC curve above 0.9 when classifying squamous epithelium versus other tissues.

Paper Details

Date Published: 29 March 2013
PDF: 10 pages
Proc. SPIE 8676, Medical Imaging 2013: Digital Pathology, 86760X (29 March 2013); doi: 10.1117/12.2007800
Show Author Affiliations
Benjamin Bryant, Texas Tech Univ. (United States)
Hamed Sari-Sarraf, Texas Tech Univ. (United States)
Mitchell Wachtel, Texas Tech Univ. (United States)
Rodney Long, National Library of Medicine (United States)
Sameer Antani, National Library of Medicine (United States)

Published in SPIE Proceedings Vol. 8676:
Medical Imaging 2013: Digital Pathology
Metin N. Gurcan; Anant Madabhushi, Editor(s)

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