Methods for Segmentation and Classification of Digital Microscopy Tissue Images

By Quoc Dang Vu, Simon Graham, Minh Nguyen Nhat To, M. Shaban, Talha Qaiser, Navid Alemi Koohbanani, S. Khurram, Tahsin M. Kurç, Keyvan Farahani, Tianhao Zhao and 4 more. Frontiers in Bioengineering and Biotechnology, 2018

349 citations recorded by the source corpus.

High-resolution microscopy images of tissue specimens provide detailed information about the morphology of normal and diseased tissue. Image analysis of tissue morphology can help cancer researchers develop a better understanding of cancer biology. Segmentation of nuclei and classification of tissue images are two common tasks in tissue image analysis. Development of accurate and efficient algorithms for these tasks is a challenging problem because of the complexity of tissue morphology and tumor heterogeneity. In this paper we present two computer algorithms; one designed for segmentation of nuclei and the other for classification of whole slide tissue images. The segmentation algorithm implements a multiscale deep residual aggregation network to accurately segment nuclear material and then separate clumped nuclei into individual nuclei. The classification algorithm initially carries out patch-level classification via a deep learning method, then patch-level statistical and morphological features are used as input to a random forest regression model for whole slide image classification. The segmentation and classification algorithms were evaluated in the MICCAI 2017 Digital Pathology challenge. The segmentation algorithm achieved an accuracy score of 0.78. The classification algorithm achieved an accuracy score of 0.81. These scores were the highest in the challenge.

Subfields: Artificial Intelligence, Biophysics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/26d5ad3849f97cfa9fa61f68330e3fa31f261668

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Citations in the corpus

Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images (2018, methodology): “The five instance segmentation datasets used within our experiments can be grouped into three groups according to their origin: TCGA (Kumar, CPM-15, CPM-17), TNBC and CoNSeP.”
AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020, positive): “Their results are used to generate quantitative summaries and maps of the size, shape, and texture of nuclei as well as statistical characterizations of spatial relationships between different types of nuclei [172]–[176].”