Pathology Image Analysis Using Segmentation Deep Learning Algorithms.
By Shidan Wang, Donghan M. Yang, Ruichen Rong, Xiaowei Zhan, Guanghua Xiao. American Journal of Pathology, 2019
332 citations recorded by the source corpus.
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, we describe the pathology image segmentation process using deep learning algorithms 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, to our knowledge, this is the first in-depth review of the applications of deep learning algorithms for segmentation in WSI analysis.
Subfields: Artificial Intelligence, Biomedical Engineering, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/c5fdba95e4b7222c304f4f9b8b3ba5fda1574387
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Citations in the corpus
- Artificial intelligence in oncology (2020, background): “17 This map is the same precise size as the input image and classifies the image by pixels.”