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Research accepted for publication in special issue of IEEE Transactions on Biomedical Engineering

CIALAB research on developing an automated computer-assisted system for follicular lymphoma grading has been accepted for publication in IEEE Transactions on Biomedical Engineering's forthcoming special issue on Multi-Parameter Optical Imaging and Image Analysis. Information regarding the publication is below:

Title: Computer-aided Detection of Centroblasts for Follicular Lymphoma Grading using Adaptive Likelihood based Cell Segmentation

Authors: Sertel O, Lozanski G, Shana'ah A, Gurcan MN

Abstract: Follicular lymphoma (FL), is one of the most common lymphoid malignancies in the western world. FL has a variable clinical course and important clinical treatment decisions for FL patients are based on histological grading, which is done by manually counting the large malignant cells called centroblasts (CB) in ten standard microscopic high power fields from H&E-stained tissue sections. This method is tedious and subjective; as a result suffers from considerable inter- and intra-reader variability even when used by expert pathologists. In this study, we present a computer-aided detection system for automated identification of CB cells from H&E-stained FL tissue samples. The proposed system uses a unitone conversion to obtain a single channel image that has the highest contrast. From the resulting image, which has a bi-modal distribution due to the H&E-stain, a cell-likelihood image is generated. Finally, a two-step CB detection procedure is applied. In the first step, we reduce evident non-CB cells based on size and shape. In the second step CB detection is further refined by learning and utilizing the texture distribution of non-CB cells. We evaluated the proposed approach on 100 region of interest images extracted from ten distinct tissue samples and obtained a promising 80.7% detection accuracy.

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