scholarly journals Cell Nuclei Classification in Histopathological Images using Hybrid O L ConvNet

2020 ◽  
Vol 16 (1s) ◽  
pp. 1-22
Author(s):  
Suvidha Tripathi ◽  
Satish Kumar Singh
Keyword(s):  
Cell Nuclei ◽  
Histopathological Images

Feature-based analysis of cell nuclei structure for classification of histopathological images

2018 ◽  
Vol 78 ◽  
pp. 152-162 ◽  
Author(s):  
Pin Wang ◽  
Sha Xu ◽  
Yongming Li ◽  
Lirui Wang ◽  
Qi Song
Keyword(s):  
Cell Nuclei ◽  
Histopathological Images ◽  
Feature Based

scholarly journals A review and comparison of breast tumor cell nuclei segmentation performances using deep convolutional neural networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Andrew Lagree ◽  
Majidreza Mohebpour ◽  
Nicholas Meti ◽  
Khadijeh Saednia ◽  
Fang-I. Lu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Breast Tissue ◽  
Superior Performance ◽  
Histopathological Analysis ◽  
Cell Nuclei ◽  
Deep Convolutional Neural Networks ◽  
Nuclei Segmentation ◽  
Histopathological Images ◽  
Biopsy Examination

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.

scholarly journals Efficient Stain-Aware Nuclei Segmentation Deep Learning Framework for Multi-Center Histopathological Images

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 954
Author(s):  
Loay Hassan ◽  
Mohamed Abdel-Nasser ◽  
Adel Saleh ◽  
Osama A. Omer ◽  
Domenec Puig
Keyword(s):  
Deep Learning ◽  
Choquet Integral ◽  
Adjacent Cell ◽  
Cell Nuclei ◽  
Nuclei Segmentation ◽  
Learning Framework ◽  
Segmentation Methods ◽  
Histopathological Images ◽  
The Individual ◽  
Whole Slide Images

Existing nuclei segmentation methods have obtained limited results with multi-center and multi-organ whole-slide images (WSIs) due to the use of different stains, scanners, overlapping, clumped nuclei, and the ambiguous boundary between adjacent cell nuclei. In an attempt to address these problems, we propose an efficient stain-aware nuclei segmentation method based on deep learning for multi-center WSIs. Unlike all related works that exploit a single-stain template from the dataset to normalize WSIs, we propose an efficient algorithm to select a set of stain templates based on stain clustering. Individual deep learning models are trained based on each stain template, and then, an aggregation function based on the Choquet integral is employed to combine the segmentation masks of the individual models. With a challenging multi-center multi-organ WSIs dataset, the experimental results demonstrate that the proposed method outperforms the state-of-art nuclei segmentation methods with aggregated Jaccard index (AJI) and F1-scores of 73.23% and 89.32%, respectively, while achieving a lower number of parameters.

scholarly journals Automatic extraction of cell nuclei from H&E-stained histopathological images

2017 ◽  
Vol 4 (2) ◽  
pp. 027502 ◽  
Author(s):  
Faliu Yi ◽  
Junzhou Huang ◽  
Lin Yang ◽  
Yang Xie ◽  
Guanghua Xiao
Keyword(s):  
Automatic Extraction ◽  
Cell Nuclei ◽  
Histopathological Images

scholarly journals Upgraded Segmentation of Histopathological Images for Classification of Intraductal Breast Lesions

2019 ◽  
Vol 8 (2) ◽  
pp. 335-338
Keyword(s):  
Breast Cancer ◽  
Clustering Algorithm ◽  
Disease Classification ◽  
Research Subjects ◽  
Cell Nuclei ◽  
Segmentation Algorithms ◽  
Histopathological Images ◽  
Prime Objective ◽  
Inner Region

Cancer among women and the second most common cancer in the world is Breast Cancer(BC). This type of cancer-initiating from breast tissue, mostly from the inner region of milk ducts. The current progress in high-throughput and getting of digitized histological studies have made it possible to use histological pattern with image analysis to facilitate disease classification using computer-aided technology (CAT). The practice of analysis has become a part of the routine clinical discovery of breast cancer. In fact, CAT has become recent research subjects in the diagnostic of medical imaging and radiology. The vast increase in the capability of image acquisition and computational power in recent decades has prompted the development of several image segmentation algorithms. For the analysis of histopathological images, the automatic dissection of cell nuclei is an important stage. Its prime objective is to determine the exact location of the nuclei and boundary points of the cells. To accurately model the preference for histological structures (ducts, vessels, tumor nets, adipose, etc.). In the proposed method, additional k means clustering algorithm used for evaluating segmentation algorithms. Here demonstrate the in proposed methods over the state-of-the-art system in performance measures.

Efficient Segmentation of Cell Nuclei in Histopathological Images

2020 ◽  
Author(s):  
Oscar Cuadros Linares ◽  
Aurea Aurea Soriano-Vargas ◽  
Bruno S. Faical ◽  
Bernd Hamann ◽  
Alexandre T. Fabro ◽  
...  
Keyword(s):  
Cell Nuclei ◽  
Histopathological Images
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