Automated mitosis detection based on combination of effective textural and morphological features from breast cancer histology slide images

2015 ◽  
Author(s):  
Fattaneh Pourakpour ◽  
Hassan Ghassemian
Keyword(s):  
Breast Cancer ◽  
Morphological Features ◽  
Histology Slide ◽  
Mitosis Detection

scholarly journals Morphological Estimation of Cellularity on Neo-Adjuvant Treated Breast Cancer Histological Images

2020 ◽  
Vol 6 (10) ◽  
pp. 101
Author(s):  
Mauricio Alberto Ortega-Ruiz ◽  
Cefa Karabağ ◽  
Victor García Garduño ◽  
Constantino Carlos Reyes-Aldasoro
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Morphological Features ◽  
Training Data ◽  
Morphological Operations ◽  
Morphological Parameters ◽  
Learning Approaches ◽  
Residual Cancer Burden ◽  
Histological Images ◽  
Treated Breast

This paper describes a methodology that extracts key morphological features from histological breast cancer images in order to automatically assess Tumour Cellularity (TC) in Neo-Adjuvant treatment (NAT) patients. The response to NAT gives information on therapy efficacy and it is measured by the residual cancer burden index, which is composed of two metrics: TC and the assessment of lymph nodes. The data consist of whole slide images (WSIs) of breast tissue stained with Hematoxylin and Eosin (H&E) released in the 2019 SPIE Breast Challenge. The methodology proposed is based on traditional computer vision methods (K-means, watershed segmentation, Otsu’s binarisation, and morphological operations), implementing colour separation, segmentation, and feature extraction. Correlation between morphological features and the residual TC after a NAT treatment was examined. Linear regression and statistical methods were used and twenty-two key morphological parameters from the nuclei, epithelial region, and the full image were extracted. Subsequently, an automated TC assessment that was based on Machine Learning (ML) algorithms was implemented and trained with only selected key parameters. The methodology was validated with the score assigned by two pathologists through the intra-class correlation coefficient (ICC). The selection of key morphological parameters improved the results reported over other ML methodologies and it was very close to deep learning methodologies. These results are encouraging, as a traditionally-trained ML algorithm can be useful when limited training data are available preventing the use of deep learning approaches.

Non-Mass Enhancement on MRI

2018 ◽  
Author(s):  
Diana L. Lam ◽  
Habib Rahbar
Keyword(s):  
Breast Cancer ◽  
Clinical Features ◽  
Morphological Features ◽  
Post Contrast ◽  
Imaging Protocols ◽  
Background Parenchymal Enhancement ◽  
Management Recommendations ◽  
Normal Background ◽  
Mass Enhancement ◽  
Parenchymal Enhancement

Breast cancer presents on MRI as an enhancing finding on post-contrast T1-weighted images that is distinct from normal background parenchymal enhancement (BPE), and these enhancing lesions can be further described as a focus, mass, or non-mass enhancement (NME). Each enhancing lesion, with the exception of a focus, can be described further with specific morphological features that are defined by the ACR BI-RADS Atlas. This chapter reviews the key imaging and clinical features, imaging protocols and pitfalls, differential diagnoses, and management recommendations of a focus of enhancement and non-mass enhancement on MRI. Topics discussed include distinguishing a focus from normal BPE, benign versus suspicious features of a focus, NME characterization, and kinetic enhancement curves.

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.

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