A Template Matching Model for Nuclear Segmentation in Digital Images of H&E Stained Slides

Abstract

Pathology has become increasingly more reliant on digital imaging as a means for viewing, sharing, and archiving slides, and as an essential first step for the application of advanced image analysis to support cancer diagnostics. In H&E stained tissue, cell nuclei are especially prominent, and their shapes, staining attributes, and distributions within the tissue serve as important diagnostic and prognostic features. Therefore, the ability to accurately identify and segment nuclei from other tissue structures is paramount toward developing a reliable analytical tool. We developed an algorithm that rapidly identifies candidate nuclei and segments them in a manner that retains much of the shape information and location precision. The algorithm uses color analysis, template matching based on shape, and clump splitting to demarcate individual nuclei and to segregate overlapping nuclei. Given its speed and relative simplicity, this method is especially amenable to processing large image regions at high magnification, making high throughput and on-demand analysis realizable.