Automatic Nuclei Detection in Histopathological Images based on Convolutional Neural Networks

AbstractBreast cancer is currently the second most common cause of cancer-related death in women. Presently, the clinical benchmark in cancer diagnosis is tissue biopsy examination. However, the manual process of histopathological analysis is laborious, time-consuming, and limited by the quality of the specimen and the experience of the pathologist. This study's objective was to determine if deep convolutional neural networks can be trained, with transfer learning, on a set of histopathological images independent of breast tissue to segment tumor nuclei of the breast. Various deep convolutional neural networks were evaluated for the study, including U-Net, Mask R-CNN, and a novel network (GB U-Net). The networks were trained on a set of Hematoxylin and Eosin (H&E)-stained images of eight diverse types of tissues. GB U-Net demonstrated superior performance in segmenting sites of invasive diseases (AJI = 0.53, mAP = 0.39 & AJI = 0.54, mAP = 0.38), validated on two hold-out datasets exclusively containing breast tissue images of approximately 7,582 annotated cells. The results of the networks, trained on images independent of breast tissue, demonstrated that tumor nuclei of the breast could be accurately segmented.

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Impact of JPEG 2000 compression on deep convolutional neural networks for metastatic cancer detection in histopathological images

Journal of Medical Imaging ◽

10.1117/1.jmi.6.2.027501 ◽

2019 ◽

Vol 6 (02) ◽

pp. 1 ◽

Cited By ~ 2

Author(s):

Farhad Ghazvinian Zanjani ◽

Svitlana Zinger ◽

Bastian Piepers ◽

Saeed Mahmoudpour ◽

Peter Schelkens

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Cancer Detection ◽

Metastatic Cancer ◽

Jpeg 2000 ◽

Deep Convolutional Neural Networks ◽

Histopathological Images

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Prognostic analysis of histopathological images using pre-trained convolutional neural networks: application to hepatocellular carcinoma

PeerJ ◽

10.7717/peerj.8668 ◽

2020 ◽

Vol 8 ◽

pp. e8668 ◽

Cited By ~ 3

Author(s):

Liangqun Lu ◽

Bernie J. Daigle

Keyword(s):

Hepatocellular Carcinoma ◽

Neural Networks ◽

Convolutional Neural Networks ◽

Disease Free Survival ◽

Disease Diagnosis ◽

Image Features ◽

Biological Pathways ◽

Survival Prediction ◽

Cox Regression Analysis ◽

Histopathological Images

Histopathological images contain rich phenotypic descriptions of the molecular processes underlying disease progression. Convolutional neural networks, state-of-the-art image analysis techniques in computer vision, automatically learn representative features from such images which can be useful for disease diagnosis, prognosis, and subtyping. Hepatocellular carcinoma (HCC) is the sixth most common type of primary liver malignancy. Despite the high mortality rate of HCC, little previous work has made use of CNN models to explore the use of histopathological images for prognosis and clinical survival prediction of HCC. We applied three pre-trained CNN models—VGG 16, Inception V3 and ResNet 50—to extract features from HCC histopathological images. Sample visualization and classification analyses based on these features showed a very clear separation between cancer and normal samples. In a univariate Cox regression analysis, 21.4% and 16% of image features on average were significantly associated with overall survival (OS) and disease-free survival (DFS), respectively. We also observed significant correlations between these features and integrated biological pathways derived from gene expression and copy number variation. Using an elastic net regularized Cox Proportional Hazards model of OS constructed from Inception image features, we obtained a concordance index (C-index) of 0.789 and a significant log-rank test (p = 7.6E−18). We also performed unsupervised classification to identify HCC subgroups from image features. The optimal two subgroups discovered using Inception model image features showed significant differences in both overall (C-index = 0.628 and p = 7.39E−07) and DFS (C-index = 0.558 and p = 0.012). Our work demonstrates the utility of extracting image features using pre-trained models by using them to build accurate prognostic models of HCC as well as highlight significant correlations between these features, clinical survival, and relevant biological pathways. Image features extracted from HCC histopathological images using the pre-trained CNN models VGG 16, Inception V3 and ResNet 50 can accurately distinguish normal and cancer samples. Furthermore, these image features are significantly correlated with survival and relevant biological pathways.

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