An adaptive digital stain separation method for deep learning-based automatic cell profile counts.

Affiliation

Dave P(1), Alahmari S(2), Goldgof D(2), Hall LO(2), Morera H(2), Mouton PR(3).
Author information:
(1)Department of Computer Science and Engineering, University of South Florida, Tampa, FL 33620, USA. Electronic address: [Email]
(2)Department of Computer Science and Engineering, University of South Florida, Tampa, FL 33620, USA.
(3)Department of Computer Science and Engineering, University of South Florida, Tampa, FL 33620, USA; SRC Biosciences, Tampa, FL 33606, USA.

Abstract

BACKGROUND: Quantifying cells in a defined region of biological tissue is critical for many clinical and preclinical studies, especially in the fields of pathology, toxicology, cancer and behavior. As part of a program to develop accurate, precise and more efficient automatic approaches for quantifying morphometric changes in biological tissue, we have shown that both deep learning-based and hand-crafted algorithms can estimate the total number of histologically stained cells at their maximal profile of focus in Extended Depth of Field (EDF) images. Deep learning-based approaches show accuracy comparable to manual counts on EDF images but significant enhancement in reproducibility, throughput efficiency and reduced error from human factors. However, a majority of the automated counts are designed for single-immunostained tissue sections. NEW METHOD: To expand the automatic counting methods to more complex dual-staining protocols, we developed an adaptive method to separate stain color channels on images from tissue sections stained by a primary immunostain with secondary counterstain. COMPARISON WITH EXISTING METHODS: The proposed method overcomes the limitations of the state-of-the-art stain-separation methods, like the requirement of pure stain color basis as a prerequisite or stain color basis learning on each image. RESULTS: Experimental results are presented for automatic counts using deep learning-based and hand-crafted algorithms for sections immunostained for neurons (Neu-N) or microglial cells (Iba-1) with cresyl violet counterstain. CONCLUSION: Our findings show more accurate counts by deep learning methods compared to the handcrafted method. Thus, stain-separated images can function as input for automatic deep learning-based quantification methods designed for single-stained tissue sections.