Integration of Deep Learning and Graph Theory for Analyzing Histopathology Whole-slide Images

Jung, Hyun; Suloway, Christian; Miao, Tianyi; Edmondson, Elijah F.; Morcock, David R.; Deléage, Claire; Liu, Yanling; Collins, Jack; Lisle, Curtis

doi:10.1109/aipr.2018.8707424

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…Dekker et al 20 assessed breast tumor stromal organization by manually drawing straight lines along stromal fibers. To segment collagen deposition in histology images, Jung et al 21 generated annotations for ANN semi-automatically by image thresholding and subsequent manual refinement. In our study, we adopted annotations similar to Jung et al 21 to train the M3 ANN model.…”

Section: Discussionmentioning

confidence: 99%

“…To segment collagen deposition in histology images, Jung et al 21 generated annotations for ANN semi-automatically by image thresholding and subsequent manual refinement. In our study, we adopted annotations similar to Jung et al 21 to train the M3 ANN model. We also expanded the approach by Dekker et al 20 to train M1 and M2 ANNs to investigate the influence of cognitive bias on collagen perception by a human expert and found that human visual perception of tissue collagen framework is highly subjective, as experts' annotations did differ significantly by all aspects evaluated (see Supplementary Table S1).…”

Section: Discussionmentioning

confidence: 99%

“…Few studies explore the feasibility of neural networks to detect and segment tissue collagen in bright-field microscopy images. Jung et al 21 presented deep convolutional neural networks applied to tissue collagen detection. Graph analytics was applied to collagen deposits segmented by a neural network from histopathology images of simian immunodeficiency virus-infected rhesus monkeys to detect collagen morphological changes in the course of infection.…”

Section: Tumor Collagen Framework From Bright-field Histology Images ...mentioning

confidence: 99%

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Tumor collagen framework from bright-field histology images predicts overall survival of breast carcinoma patients

Morkunas

Zilenaite

Laurinavičienė

et al. 2021

Sci Rep

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Within the tumor microenvironment, specifically aligned collagen has been shown to stimulate tumor progression by directing the migration of metastatic cells along its structural framework. Tumor-associated collagen signatures (TACS) have been linked to breast cancer patient outcome. Robust and affordable methods for assessing biological information contained in collagen architecture need to be developed. We have developed a novel artificial neural network (ANN) based approach for tumor collagen segmentation from bright-field histology images and have tested it on a set of tissue microarray sections from early hormone receptor-positive invasive ductal breast carcinoma stained with Sirius Red (1 core per patient, n = 92). We designed and trained ANNs on sets of differently annotated image patches to segment collagen fibers and extracted 37 features of collagen fiber morphometry, density, orientation, texture, and fractal characteristics in the entire cohort. Independent instances of ANN models trained on highly differing annotations produced reasonably concordant collagen segmentation masks and allowed reliable prognostic Cox regression models (with likelihood ratios 14.11–22.99, at p-value < 0.05) superior to conventional clinical parameters (size of the primary tumor (T), regional lymph node status (N), histological grade (G), and patient age). Additionally, we noted statistically significant differences of collagen features between tumor grade groups, and the factor analysis revealed features resembling the TACS concept. Our proposed method offers collagen framework segmentation from bright-field histology images and provides novel image-based features for better breast cancer patient prognostication.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Tumor Collagen Framework From Bright-field Histology Images ...mentioning

confidence: 99%

See 1 more Smart Citation

Tumor collagen framework from bright-field histology images predicts overall survival of breast carcinoma patients

Morkunas

Zilenaite

Laurinavičienė

et al. 2021

Sci Rep

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Nipah Virus-Associated Neuropathology in African Green Monkeys During Acute Disease and Convalescence

Goldin,

Liu,

Rosenke

et al. 2024

The Journal of Infectious Diseases

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Background Nipah virus is an emerging zoonotic virus that causes severe respiratory disease and meningoencephalitis. The pathophysiology of Nipah virus meningoencephalitis is poorly understood. Methods We have collected the brains of African green monkeys during multiple Nipah virus, Bangladesh studies, resulting in 14 brains with Nipah virus-associated lesions. Results The lesions seen in the brain of African green monkeys infected with Nipah virus, Bangladesh were very similar to those observed in humans with Nipah virus, Malaysia infection. We observed viral RNA and antigen within neurons and endothelial cells, within encephalitis foci and in uninflamed portions of the CNS. CD8+ T cells had a consistently high prevalence in CNS lesions. We developed a UNet model for quantifying and visualizing inflammation in the brain in a high-throughput and unbiased manner. While CD8+ T cells had a consistently high prevalence in CNS lesions, the model revealed that CD68+ cells were numerically the immune cell with the highest prevalence in the CNS of NiV-infected animals. Conclusion Our study provides an in-depth analysis on Nipah virus infection in the brains of primates, and similarities between lesions in patients and the animals in our study validate this model.

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Application of graph-based features in computer-aided diagnosis for histopathological image classification of gastric cancer

Zhang

et al. 2022

Digital Medicine

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Background: The gold standard for gastric cancer detection is gastric histopathological image analysis, but there are certain drawbacks in the existing histopathological detection and diagnosis. Method: In this paper, based on the study of computer-aided diagnosis (CAD) system, graph-based features are applied to gastric cancer histopathology microscopic image analysis, and a classifier is used to classify gastric cancer cells from benign cells. Firstly, image segmentation is performed. After finding the region, cell nuclei are extracted using the k-means method, the minimum spanning tree (MST) is drawn, and graph-based features of the MST are extracted. The graph-based features are then put into the classifier for classification. Result: Different segmentation methods are compared in the tissue segmentation stage, among which are Level-Set, Otsu thresholding, watershed, SegNet, U-Net and Trans-U-Net segmentation; Graph-based features, Red, Green, Blue features, Grey-Level Co-occurrence Matrix features, Histograms of Oriented Gradient features and Local Binary Patterns features are compared in the feature extraction stage; Radial Basis Function (RBF) Support Vector Machine (SVM), Linear SVM, Artificial Neural Network, Random Forests, k-NearestNeighbor, VGG16, and Inception-V3 are compared in the classifier stage. It is found that using U-Net to segment tissue areas, then extracting graph-based features, and finally using RBF SVM classifier gives the optimal results with 94.29%. Conclusion: This paper focus on a graph-based features microscopic image analysis method for gastric cancer histopathology. The final experimental data shows that our analysis method is better than other methods in classifying histopathological images of gastric cancer.

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Integration of Deep Learning and Graph Theory for Analyzing Histopathology Whole-slide Images

Cited by 3 publications

References 9 publications

Tumor collagen framework from bright-field histology images predicts overall survival of breast carcinoma patients

Tumor collagen framework from bright-field histology images predicts overall survival of breast carcinoma patients

Nipah Virus-Associated Neuropathology in African Green Monkeys During Acute Disease and Convalescence

Application of graph-based features in computer-aided diagnosis for histopathological image classification of gastric cancer

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