Tumor Segmentation in Contrast-Enhanced Magnetic Resonance Imaging for Nasopharyngeal Carcinoma: Deep Learning with Convolutional Neural Network
المؤلفون المشاركون
Huang, Bingsheng
Li, Qiaoliang
Xu, Yuzhen
Chen, Zhewei
Liu, Dexiang
Feng, Shi-Ting
Law, Martin
Ye, Yufeng
المصدر
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-7، 7ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-10-17
دولة النشر
مصر
عدد الصفحات
7
التخصصات الرئيسية
الملخص EN
Objectives.
To evaluate the application of a deep learning architecture, based on the convolutional neural network (CNN) technique, to perform automatic tumor segmentation of magnetic resonance imaging (MRI) for nasopharyngeal carcinoma (NPC).
Materials and Methods.
In this prospective study, 87 MRI containing tumor regions were acquired from newly diagnosed NPC patients.
These 87 MRI were augmented to >60,000 images.
The proposed CNN network is composed of two phases: feature representation and scores map reconstruction.
We designed a stepwise scheme to train our CNN network.
To evaluate the performance of our method, we used case-by-case leave-one-out cross-validation (LOOCV).
The ground truth of tumor contouring was acquired by the consensus of two experienced radiologists.
Results.
The mean values of dice similarity coefficient, percent match, and their corresponding ratio with our method were 0.89±0.05, 0.90±0.04, and 0.84±0.06, respectively, all of which were better than reported values in the similar studies.
Conclusions.
We successfully established a segmentation method for NPC based on deep learning in contrast-enhanced magnetic resonance imaging.
Further clinical trials with dedicated algorithms are warranted.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Li, Qiaoliang& Xu, Yuzhen& Chen, Zhewei& Liu, Dexiang& Feng, Shi-Ting& Law, Martin…[et al.]. 2018. Tumor Segmentation in Contrast-Enhanced Magnetic Resonance Imaging for Nasopharyngeal Carcinoma: Deep Learning with Convolutional Neural Network. BioMed Research International،Vol. 2018, no. 2018, pp.1-7.
https://search.emarefa.net/detail/BIM-1129513
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Li, Qiaoliang…[et al.]. Tumor Segmentation in Contrast-Enhanced Magnetic Resonance Imaging for Nasopharyngeal Carcinoma: Deep Learning with Convolutional Neural Network. BioMed Research International No. 2018 (2018), pp.1-7.
https://search.emarefa.net/detail/BIM-1129513
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Li, Qiaoliang& Xu, Yuzhen& Chen, Zhewei& Liu, Dexiang& Feng, Shi-Ting& Law, Martin…[et al.]. Tumor Segmentation in Contrast-Enhanced Magnetic Resonance Imaging for Nasopharyngeal Carcinoma: Deep Learning with Convolutional Neural Network. BioMed Research International. 2018. Vol. 2018, no. 2018, pp.1-7.
https://search.emarefa.net/detail/BIM-1129513
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1129513
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