A neural classifier enabling high-throughput topological analysis of lymphocytes in tissue sections.

Abstract

A neural cell detection system (NCDS) for the automatic quantitation of fluorescent lymphocytes in tissue sections is presented in this paper. The system acquires visual knowledge from a set of training cell-image patches selected by a user. The trained system evaluates an image in 2 min calculating: the number, the positions, and the phenotypes of the fluorescent cells. For validation, the NCDS learning performance was tested by cross validation on digitized images of tissue sections obtained from inherently different types of tissue: diagnostic tissue sections across the human tonsil and across an inflammatory lymphocyte infiltrate of the human skeletal muscle. The NCDS detection results were compared with detection results from biomedical experts and were visually evaluated by our most experienced biomedical expert. Although the micrographs were noisy and the fluorescent cells varied in shape and size, the NCDS detected a minimum of 95% of the cells. In contrast, the cellular counts based on visual cell recognition of the experts were inconsistent and largely unreproducible for approximately 80% of the lymphocytes present in a visual field. The data indicate that the NCDS is rapid and delivers highly reproducible results and, therefore, enables high-throughput topological screening of lymphocytes in many types of tissue, e.g., as obtained by routine diagnostic biopsy procedures. High-throughput screening with the NCDS provides the platform for the quantitative analysis of the interrelationship between tissue environment, cellular phenotype, and cellular topology.