Pathology Image Analysis Using Segmentation Deep Learning Algorithms

Shidan Wang, Donghan M. Yang, Ruichen Rong, Xiaowei Zhan, Guanghua Xiao

Research output: Contribution to journalReview article

12 Scopus citations

Abstract

With the rapid development of image scanning techniques and visualization software, whole slide imaging (WSI) is becoming a routine diagnostic method. Accelerating clinical diagnosis from pathology images and automating image analysis efficiently and accurately remain significant challenges. Recently, deep learning algorithms have shown great promise in pathology image analysis, such as in tumor region identification, metastasis detection, and patient prognosis. Many machine learning algorithms, including convolutional neural networks, have been proposed to automatically segment pathology images. Among these algorithms, segmentation deep learning algorithms such as fully convolutional networks stand out for their accuracy, computational efficiency, and generalizability. Thus, deep learning–based pathology image segmentation has become an important tool in WSI analysis. In this review, the pathology image segmentation process using deep learning algorithms is described in detail. The goals are to provide quick guidance for implementing deep learning into pathology image analysis and to provide some potential ways of further improving segmentation performance. Although there have been previous reviews on using machine learning methods in digital pathology image analysis, this is the first in-depth review of the applications of deep learning algorithms for segmentation in WSI analysis.

Original languageEnglish (US)
Pages (from-to)1686-1698
Number of pages13
JournalAmerican Journal of Pathology
Volume189
Issue number9
DOIs
StatePublished - Sep 1 2019

ASJC Scopus subject areas

  • Pathology and Forensic Medicine

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