Pathological Image Analysis Using the GPU: Stroma Classification for Neuroblastoma

Authors:
Antonio Ruiz;Olcay Sertel;Manuel Ujaldon;Umit Catalyurek;Joel Saltz;Metin Gurcan
Affiliations:
-;-;-;-;-;-
Venue:
BIBM '07 Proceedings of the 2007 IEEE International Conference on Bioinformatics and Biomedicine
Year:
2007

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Abstract

Neuroblastoma is one of the most malignant childhood cancers affecting infants mostly. The current prognosis is based on microscopic examination of slides by expert pathologists, a process that is error-prone, time consuming and may lead to inter- and intra-reader variations. Therefore, we are developing a Computer Aided Prognosis (CAP) system which provides computerized image analysis t o assist pathologist in their prognosis. Since this system operates on relatively largescale images and requires sophisticated algorithms, it takes a long time to process whole-slide images. I n this paper, we propose a novel and eficient approach for the execution of a CAP system for neuroblastoma prognosis, using the graphics processing unit (GPU). B y leveraging high memory bandwidth and strong floating point operation capabilities of the GPU, our goal is t o achieve order of magnitude reduction in the overall execution time as compared t o that on a CPU alone. The proposed approach was tested on a set of testing images with a promising accuracy of 99.4% and an execution performance gain factor up to 45 times compared t o C++ code running on the CPU.