Deep Convolutional Neural Networks-Based Automatic Breast Segmentation and Mass Detection in DCE-MRI
المؤلفون المشاركون
Pang, Zhiyong
Jiao, Han
Jiang, Xinhua
Lin, Xiaofeng
Huang, Yihua
Li, Li
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-05-05
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
الملخص 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.
نمط استشهاد جمعية علماء النفس الأمريكية (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
نمط استشهاد الجمعية الأمريكية للغات الحديثة (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
نمط استشهاد الجمعية الطبية الأمريكية (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
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1139366
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر