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

Understand

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.