Automatic Semantic Segmentation of Brain Gliomas from MRI Images Using a Deep Cascaded Neural Network
المؤلفون المشاركون
Jiang, Jingfeng
Cui, Shaoguo
Mao, Lei
Liu, Chang
Xiong, Shuyu
المصدر
Journal of Healthcare Engineering
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-14، 14ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-03-19
دولة النشر
مصر
عدد الصفحات
14
التخصصات الرئيسية
الملخص EN
Brain tumors can appear anywhere in the brain and have vastly different sizes and morphology.
Additionally, these tumors are often diffused and poorly contrasted.
Consequently, the segmentation of brain tumor and intratumor subregions using magnetic resonance imaging (MRI) data with minimal human interventions remains a challenging task.
In this paper, we present a novel fully automatic segmentation method from MRI data containing in vivo brain gliomas.
This approach can not only localize the entire tumor region but can also accurately segment the intratumor structure.
The proposed work was based on a cascaded deep learning convolutional neural network consisting of two subnetworks: (1) a tumor localization network (TLN) and (2) an intratumor classification network (ITCN).
The TLN, a fully convolutional network (FCN) in conjunction with the transfer learning technology, was used to first process MRI data.
The goal of the first subnetwork was to define the tumor region from an MRI slice.
Then, the ITCN was used to label the defined tumor region into multiple subregions.
Particularly, ITCN exploited a convolutional neural network (CNN) with deeper architecture and smaller kernel.
The proposed approach was validated on multimodal brain tumor segmentation (BRATS 2015) datasets, which contain 220 high-grade glioma (HGG) and 54 low-grade glioma (LGG) cases.
Dice similarity coefficient (DSC), positive predictive value (PPV), and sensitivity were used as evaluation metrics.
Our experimental results indicated that our method could obtain the promising segmentation results and had a faster segmentation speed.
More specifically, the proposed method obtained comparable and overall better DSC values (0.89, 0.77, and 0.80) on the combined (HGG + LGG) testing set, as compared to other methods reported in the literature.
Additionally, the proposed approach was able to complete a segmentation task at a rate of 1.54 seconds per slice.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Cui, Shaoguo& Mao, Lei& Jiang, Jingfeng& Liu, Chang& Xiong, Shuyu. 2018. Automatic Semantic Segmentation of Brain Gliomas from MRI Images Using a Deep Cascaded Neural Network. Journal of Healthcare Engineering،Vol. 2018, no. 2018, pp.1-14.
https://search.emarefa.net/detail/BIM-1187329
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Cui, Shaoguo…[et al.]. Automatic Semantic Segmentation of Brain Gliomas from MRI Images Using a Deep Cascaded Neural Network. Journal of Healthcare Engineering No. 2018 (2018), pp.1-14.
https://search.emarefa.net/detail/BIM-1187329
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Cui, Shaoguo& Mao, Lei& Jiang, Jingfeng& Liu, Chang& Xiong, Shuyu. Automatic Semantic Segmentation of Brain Gliomas from MRI Images Using a Deep Cascaded Neural Network. Journal of Healthcare Engineering. 2018. Vol. 2018, no. 2018, pp.1-14.
https://search.emarefa.net/detail/BIM-1187329
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1187329
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