An efficient computational framework for the analysis of whole slide images: Application to follicular lymphoma immunohistochemistry

• Published in: Journal of computational science Vol. 3; no. 5; pp. 269 - 279
• Main Authors: Samsi, Siddharth, Krishnamurthy, Ashok K., Gurcan, Metin N.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.09.2012
• Subjects: CD10; Follicular lymphoma; Immunohistochemical; K-means; Parallel computing; K-means; Follicular lymphoma; Parallel computing; Immunohistochemical; CD10
• ISSN: 1877-7503; 1877-7511
• DOI: 10.1016/j.jocs.2012.01.009

Follicular lymphoma (FL) is one of the most common non-Hodgkin lymphoma in the United States. Diagnosis and grading of FL is based on the review of histopathological tissue sections under a microscope and is influenced by human factors such as fatigue and reader bias. Computer-aided image analysis tools can help improve the accuracy of diagnosis and grading and act as another tool at the pathologist's disposal. Our group has been developing algorithms for identifying follicles in immunohistochemical images. These algorithms have been tested and validated on small images extracted from whole slide images. However, the use of these algorithms for analyzing the entire whole slide image requires significant changes to the processing methodology since the images are relatively large (on the order of 100k×100k pixels). In this paper we discuss the challenges involved in analyzing whole slide images and propose potential computational methodologies for addressing these challenges. We discuss the use of parallel computing tools on commodity clusters and compare performance of the serial and parallel implementations of our approach.