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29 March 2013 Fast GPU-based segmentation of H&E stained squamous epithelium from multi-gigapixel tiled virtual slides
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Proceedings Volume 8676, Medical Imaging 2013: Digital Pathology; 86760X (2013)
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States
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.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Benjamin Bryant, Hamed Sari-Sarraf, Mitchell Wachtel, Rodney Long, and Sameer Antani "Fast GPU-based segmentation of H&E stained squamous epithelium from multi-gigapixel tiled virtual slides", Proc. SPIE 8676, Medical Imaging 2013: Digital Pathology, 86760X (29 March 2013);

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