Segmentation of follicles from CD8-stained slides of follicular lymphoma using deep learning.

Follicular Lymphoma (FL) is the second most common subtype of lymphoma in the Western World. It is a low-grade lymphoma arising from Germinal Centre (GC) B cells. The neoplasm predominantly consists of back-to-back arrangement of nodules or follicles of transformed GC B cells with the replacement of lymph node architecture and loss of normal cortex and medullary differentiation, which is preserved in nonneoplastic or reactive lymph node. There is a growing interest in studying different cell subsets inside and on the periphery of the follicles to direct curative therapies and minimize treatment-related complications. To facilitate this analysis, we develop an automated method for follicle detection from images of CD8 stained histopathological slides. The proposed method is trained on eight whole digital slides. The method is inspired by U-net to segment follicles from the whole slide images. The results on an independent dataset resulted in an average Dice similarity coefficient of 85.6% when compared to an expert pathologist's annotations. We expect that the method will play a considerable role for comparing the ratios of different subsets of cells inside and at the periphery of the follicles.