Computer-aided detection of centroblasts for follicular lymphoma grading using adaptive likelihood-based cell segmentation.

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 intrareader variability even when used by expert pathologists. In this paper, 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 bimodal 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 identify evident nonCB cells based on size and shape. In the second step, the CB detection is further refined by learning and utilizing the texture distribution of nonCB 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.