Deep Convolutional Neural Networks-Based Automatic Breast Segmentation and Mass Detection in DCE-MRI
Joint Authors
Pang, Zhiyong
Jiao, Han
Jiang, Xinhua
Lin, Xiaofeng
Huang, Yihua
Li, Li
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-05-05
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Breast segmentation and mass detection in medical images are important for diagnosis and treatment follow-up.
Automation of these challenging tasks can assist radiologists by reducing the high manual workload of breast cancer analysis.
In this paper, deep convolutional neural networks (DCNN) were employed for breast segmentation and mass detection in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).
First, the region of the breasts was segmented from the remaining body parts by building a fully convolutional neural network based on U-Net++.
Using the method of deep learning to extract the target area can help to reduce the interference external to the breast.
Second, a faster region with convolutional neural network (Faster RCNN) was used for mass detection on segmented breast images.
The dataset of DCE-MRI used in this study was obtained from 75 patients, and a 5-fold cross validation method was adopted.
The statistical analysis of breast region segmentation was carried out by computing the Dice similarity coefficient (DSC), Jaccard coefficient, and segmentation sensitivity.
For validation of breast mass detection, the sensitivity with the number of false positives per case was computed and analyzed.
The Dice and Jaccard coefficients and the segmentation sensitivity value for breast region segmentation were 0.951, 0.908, and 0.948, respectively, which were better than those of the original U-Net algorithm, and the average sensitivity for mass detection achieved 0.874 with 3.4 false positives per case.
American Psychological Association (APA)
Jiao, Han& Jiang, Xinhua& Pang, Zhiyong& Lin, Xiaofeng& Huang, Yihua& Li, Li. 2020. Deep Convolutional Neural Networks-Based Automatic Breast Segmentation and Mass Detection in DCE-MRI. Computational and Mathematical Methods in Medicine،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1139366
Modern Language Association (MLA)
Jiao, Han…[et al.]. Deep Convolutional Neural Networks-Based Automatic Breast Segmentation and Mass Detection in DCE-MRI. Computational and Mathematical Methods in Medicine No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1139366
American Medical Association (AMA)
Jiao, Han& Jiang, Xinhua& Pang, Zhiyong& Lin, Xiaofeng& Huang, Yihua& Li, Li. Deep Convolutional Neural Networks-Based Automatic Breast Segmentation and Mass Detection in DCE-MRI. Computational and Mathematical Methods in Medicine. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1139366
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1139366