A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images

By Jun Xu, Xiaofei Luo, Guanhao Wang, Hannah Gilmore, Anant Madabhushi. Neurocomputing, 2016

458 citations recorded by the source corpus.

Epithelial (EP) and stromal (ST) are two types of tissues in histological images. Automated segmentation or classification of EP and ST tissues is important when developing computerized system for analyzing the tumor microenvironment. In this paper, a Deep Convolutional Neural Networks (DCNN) based feature learning is presented to automatically segment or classify EP and ST regions from digitized tumor tissue microarrays (TMAs). Current approaches are based on handcraft feature representation, such as color, texture, and Local Binary Patterns (LBP) in classifying two regions. Compared to handcrafted feature based approaches, which involve task dependent representation, DCNN is an end-to-end feature extractor that may be directly learned from the raw pixel intensity value of EP and ST tissues in a data driven fashion. These high-level features contribute to the construction of a supervised classifier for discriminating the two types of tissues. In this work we compare DCNN based models with three handcraft feature extraction based approaches on two different datasets which consist of 157 Hematoxylin and Eosin (H&E) stained images of breast cancer and 1376 immunohistological (IHC) stained images of colorectal cancer, respectively. The DCNN based feature learning approach was shown to have a F1 classification score of 85%, 89%, and 100%, accuracy (ACC) of 84%, 88%, and 100%, and Matthews Correlation Coefficient (MCC) of 86%, 77%, and 100% on two H&E stained (NKI and VGH) and IHC stained data, respectively. Our DNN based approach was shown to outperform three handcraft feature extraction based approaches in terms of the classification of EP and ST regions.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/b1544931434229563314ad5e80bbb5de71b3be3f

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Citations in the corpus

A Survey of Deep Learning for Scientific Discovery (2020, background): “Examples include segmenting brain MR images [156, 236], identifying key regions of cells in diﬀerent tissues [254, 217] and even studying bone structure [129].”
Pathology Image Analysis Using Segmentation Deep Learning Algorithms. (2019, methodology): “12 To perform image segmentation for large data (eg, whole slide pathology images), the image is ﬁ rst divided into many small patches.”
Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges (2019, methodology): “Meanwhile, other studies (Araujo et al. 2017; Cheng et al. 2016; Duraisamy and Emperumal 2017; Feng et al. 2018; Gandomkar et al. 2018; Kumar et al. 2017a; Spanhol et al. 2016a, 2017; Xu et al. 2016; Zheng et al. 2017) extracted many patches from the original image.”