Subtype Cell Detection with an Accelerated Deep Convolution Neural Network

Wang, Sheng; Yao, Jiawen; Xu, Zheng; Huang, Junzhou

doi:10.1007/978-3-319-46723-8_74

Cited by 50 publications

(20 citation statements)

References 13 publications

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“…The CNN is a supervised deep learning method that has been successfully applied in a large number of image analysis problems (Cireşan et al, 2013; Huang et al, 2016; Xie et al, 2015a, 2015b; Wang et al, 2016; Sirinukunwattana et al, 2016; Bayramoglu and Heikkila, 2016; Su et al, 2015; Hou et al, 2016a; Murthy et al, 2017;Chen et al, 2017; Xu and Huang, 2016). A CNN first uses a set of training data to learn a classification (or predictive) model in the training phase.…”

Section: Resultsmentioning

confidence: 99%

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

et al. 2018

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SUMMARY Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumor-infiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment.

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Section: Resultsmentioning

confidence: 99%

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

et al. 2018

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“…It is a key step in computing a categorization via imaging features of patients into groups for cohort selection and correlation analysis. Methods for segmentation and classification have been proposed by several research projects (Gurcan et al, 2009; Ghaznavi et al, 2013; Xie et al, 2015; Xu et al, 2015; Manivannan et al, 2016; Peikari and Martel, 2016; Sirinukunwattana et al, 2016; Wang et al, 2016; Xing and Yang, 2016; Al-Milaji et al, 2017; Chen et al, 2017; Zheng et al, 2017; Graham and Rajpoot, 2018; Senaras and Gurcan, 2018). Xing and Yang (2016) provide a good review of segmentation algorithms for histopathology images.…”

Section: Introductionmentioning

confidence: 99%

Methods for Segmentation and Classification of Digital Microscopy Tissue Images

Graham

Kurç

et al. 2019

Front. Bioeng. Biotechnol.

219

137

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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.

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“…For deep leaning-based models, especially CNN [29], have attracted particular attention for applying to the problem of cell counting and detection recently [30]- [32]. Different from SVM and random forests that rely on hand crafted features for object classification, CNN can automatically learn multi-level hierarchies of features that are invariant to irrelevant variations of samples while preserving relevant information [31], [33]- [35]. However, the networks were trained to perform in well-controlled environments, with clean background and little cell overlap on synthetic images.…”

Section: B Learning Based Cell Analysis Methodsmentioning

confidence: 99%

Point Supervised Extended Scenario Nuclear Analysis Framework Based on LSTM-CFCN

et al. 2020

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Cells and cell like particles detection and segmentation are of significant interest to many biological and clinical studies. Traditionally, these tasks are usually performed by visual inspection, which is time consuming, labor intensity and prone to induce subjective bias between different people. These make automatic cell analysis protocols essential for large-scale and objective studies. In recent years, imaging technical has been significantly advanced following the great success by computer vision. In addition, these technologies enable the cross module microscopy analysis, and make the task of cell analysis extremely challenging. Over these decades, computer aided cell detection, counting and segmentation have evolved from earlier filter based methods to the state-of-art deep learning protocols. However, there are still few suitable frameworks that can process multiple source cell images at the same time. In this paper, we seek a different route and propose a novel efficient framework for robust cell analysis based on Long Short Term Memory Channeled Fully Convolution Neural Networks (LSTM-CFCN). The results demonstrates that our framework is able to perform most of cell detection, counting and segmentation tasks from different cell type, and it can also cover most kinds of microscopy images scenarios including dark field, bright field, pathological and electron images. We have perform substantial experiments on several benchmark datasets, the LSTM-CFCN achieves the highest or at least top-2 performance in terms of F1-score, compared with other state-of-the-art methods. INDEX TERMS Cell analysis, LSTM-CFCN, multi-task learning, multi-scale image processing.

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Subtype Cell Detection with an Accelerated Deep Convolution Neural Network

Cited by 50 publications

References 13 publications

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Methods for Segmentation and Classification of Digital Microscopy Tissue Images

Point Supervised Extended Scenario Nuclear Analysis Framework Based on LSTM-CFCN

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