Segmenting multiple overlapping Nuclei in H[amp ]E Stained Breast Cancer Histopathology Images based on an improved watershed

2015 ◽  
Author(s):  
Pengfei Shen ◽  
Jia Gu ◽  
Jie Yang ◽  
TieXiang Wen ◽  
Wenjian Qin ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Histopathology Images

scholarly journals Artificial Intelligence-Based Mitosis Detection in Breast Cancer Histopathology Images Using Faster R-CNN and Deep CNNs

2020 ◽  
Vol 9 (3) ◽  
pp. 749 ◽  
Author(s):  
Tahir Mahmood ◽  
Muhammad Arsalan ◽  
Muhammad Owais ◽  
Min Beom Lee ◽  
Kang Ryoung Park
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Computational Cost ◽  
Mitotic Cell ◽  
Medical Doctor ◽  
Cell Detection ◽  
Generalization Capability ◽  
Mitosis Detection ◽  
Histopathology Images ◽  
Mitotic Cells

Breast cancer is the leading cause of mortality in women. Early diagnosis of breast cancer can reduce the mortality rate. In the diagnosis, the mitotic cell count is an important biomarker for predicting the aggressiveness, prognosis, and grade of breast cancer. In general, pathologists manually examine histopathology images under high-resolution microscopes for the detection of mitotic cells. However, because of the minute differences between the mitotic and normal cells, this process is tiresome, time-consuming, and subjective. To overcome these challenges, artificial-intelligence-based (AI-based) techniques have been developed which automatically detect mitotic cells in the histopathology images. Such AI techniques accelerate the diagnosis and can be used as a second-opinion system for a medical doctor. Previously, conventional image-processing techniques were used for the detection of mitotic cells, which have low accuracy and high computational cost. Therefore, a number of deep-learning techniques that demonstrate outstanding performance and low computational cost were recently developed; however, they still require improvement in terms of accuracy and reliability. Therefore, we present a multistage mitotic-cell-detection method based on Faster region convolutional neural network (Faster R-CNN) and deep CNNs. Two open datasets (international conference on pattern recognition (ICPR) 2012 and ICPR 2014 (MITOS-ATYPIA-14)) of breast cancer histopathology were used in our experiments. The experimental results showed that our method achieves the state-of-the-art results of 0.876 precision, 0.841 recall, and 0.858 F1-measure for the ICPR 2012 dataset, and 0.848 precision, 0.583 recall, and 0.691 F1-measure for the ICPR 2014 dataset, which were higher than those obtained using previous methods. Moreover, we tested the generalization capability of our technique by testing on the tumor proliferation assessment challenge 2016 (TUPAC16) dataset and found that our technique also performs well in a cross-dataset experiment which proved the generalization capability of our proposed technique.

scholarly journals Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images

2016 ◽  
Vol 35 (1) ◽  
pp. 119-130 ◽  
Author(s):  
Jun Xu ◽  
Lei Xiang ◽  
Qingshan Liu ◽  
Hannah Gilmore ◽  
Jianzhong Wu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Nuclei Detection ◽  
Sparse Autoencoder ◽  
Histopathology Images ◽  
Stacked Sparse Autoencoder

scholarly journals Segmentation of Tumour Region on Breast Histopathology Images for Assessment of Glandular Formation in Breast Cancer Grading

2021 ◽  
Vol 2071 (1) ◽  
pp. 012051
Author(s):  
P A S Nor Rahim ◽  
N Mustafa ◽  
H Yazid ◽  
T Xiao Jian ◽  
S Daud ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Median Filter ◽  
Automatic Segmentation ◽  
Post Processing ◽  
Processing Stage ◽  
Tubule Formation ◽  
Segmentation Accuracy ◽  
Three Stages ◽  
Histopathology Images ◽  
Cancer Grading

Abstract Breast cancer is the most silent killer among cancers nowadays. NHG system is widely accepted worldwide as a gold standard in providing the overall grade to breast cancer. One of the breast cancer features used in the NHG system is tubule formation. Assessment of tubule formation requires pathologist to identify tumour regions. However, colour variation on breast histopathology could influence tumour regions detection on breast histopathology images. Manual identification of tumour regions using microscope may also vary between pathologists. Thus, automatic segmentation is crucial to segment tumour regions. In this study, a simple approach of segmentation was proposed to segment tumour region on breast histopathology images. The proposed segmentation involved three stages: pre-processing, segmentation and post-processing. The proposed approach using GHE and median filter in the pre-processing stage; Otsu thresholding in the segmentation stage and; morphological operation and pixel removal in the post-processing stage was found able to segment the tumour region with average segmentation accuracy of 90.4 %.

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