Glandular Segmentation of Prostate Cancer: An Illustration of How the Choice of Histopathological Stain Is One Key to Success for Computational Pathology.

Christophe Avenel,Anca Dragomir,Ingrid B Carlbom,Anna Tolf

doi:10.3389/fbioe.2019.00125

Abstract

Digital pathology offers the potential for computer-aided diagnosis, significantly reducing the pathologists' workload and paving the way for accurate prognostication with reduced inter-and intra-observer variations. But successful computer-based analysis requires careful tissue preparation and image acquisition to keep color and intensity variations to a minimum. While the human eye may recognize prostate glands with significant color and intensity variations, a computer algorithm may fail under such conditions. Since malignancy grading of prostate tissue according to Gleason or to the International Society of Urological Pathology (ISUP) grading system is based on architectural growth patterns of prostatic carcinoma, automatic methods must rely on accurate identification of the prostate glands. But due to poor color differentiation between stroma and epithelium from the common stain hematoxylin-eosin, no method is yet able to segment all types of glands, making automatic prognostication hard to attain. We address the effect of tissue preparation on glandular segmentation with an alternative stain, Picrosirius red-hematoxylin, which clearly delineates the stromal boundaries, and couple this stain with a color decomposition that removes intensity variation. In this paper we propose a segmentation algorithm that uses image analysis techniques based on mathematical morphology and that can successfully determine the glandular boundaries. Accurate determination of the stromal and glandular morphology enables the identification of the architectural pattern that determine the malignancy grade and classify each gland into its appropriate Gleason grade or ISUP Grade Group. Segmentation of prostate tissue with the new stain and decomposition method has been successfully tested on more than 11000 objects including well-formed glands (Gleason grade 3), cribriform and fine caliber glands (grade 4), and single cells (grade 5) glands.

Highlights

Digital pathology is an emerging field, where glass tissue slides are scanned and stored as digital images for improved workflow, computer-aided analysis, and storage and management of the data
Pathologists rely on multiple, contrasting stains to analyze tissue samples, but histological stains are developed for analysis with a microscope and not for computational pathology applications
Each whole mount section was stained with Picrosirius red-hematoxylin (PSR-Htx) (Histolab products AB, Södra Långebergsgatan 36, SE421 32 Västra Frölunda, Sweden), and scanned at 20x with an Aperio AT2 whole slide scanner (Leica Biosystems) and a NanoZoomer S60 Digital slide scanner (Hamamatsu)

Summary

Introduction

Digital pathology is an emerging field, where glass tissue slides are scanned and stored as digital images for improved workflow, computer-aided analysis, and storage and management of the data. Once tissue slides are digitized, computer-aided image analysis makes it possible to enhance the resulting images digitally and to extract quantitative information to support the pathologist’s decision process. Computer-aided analysis of tissue data requires high-quality image data, where the tissue components are clearly delineated and where the stain variations and noise are kept to a minimum. Pathologists rely on multiple, contrasting stains to analyze tissue samples, but histological stains are developed for analysis with a microscope and not for computational pathology applications. In Azar et al (2013), several different histological stains were evaluated for automatic classification of components in prostate tissue. The stains were tested with both supervised and unsupervised classification methods which showed that some stains consistently outperform others according to objective error criteria

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