Deep Learning Models Delineates Multiple Nuclear Phenotypes in H&E Stained Histology Sections

TL;DR

This paper presents a deep learning approach that combines multiple convolutional networks to effectively segment and distinguish various nuclear phenotypes in histology images, overcoming challenges posed by nuclear diversity and overlaps.

Contribution

The study introduces a fusion of deep convolutional networks that learn region- and boundary-based features to improve nuclear segmentation in histology sections.

Findings

Fusion of deep networks improves segmentation accuracy.

Method effectively distinguishes overlapping nuclei.

Validated on breast and brain histology samples.

Abstract

Nuclear segmentation is an important step for profiling aberrant regions of histology sections. However, segmentation is a complex problem as a result of variations in nuclear geometry (e.g., size, shape), nuclear type (e.g., epithelial, fibroblast), and nuclear phenotypes (e.g., vesicular, aneuploidy). The problem is further complicated as a result of variations in sample preparation. It is shown and validated that fusion of very deep convolutional networks overcomes (i) complexities associated with multiple nuclear phenotypes, and (ii) separation of overlapping nuclei. The fusion relies on integrating of networks that learn region- and boundary-based representations. The system has been validated on a diverse set of nuclear phenotypes that correspond to the breast and brain histology sections.