scholarly journals Whole brain segmentation with full volume neural network

2021 ◽  
Vol 93 ◽  
pp. 101991
Author(s):  
Yeshu Li ◽  
Jonathan Cui ◽  
Yilun Sheng ◽  
Xiao Liang ◽  
Jingdong Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Brain Segmentation ◽  
Whole Brain ◽  
Full Volume

scholarly journals PSACNN: Pulse sequence adaptive fast whole brain segmentation

NeuroImage ◽  
2019 ◽  
Vol 199 ◽  
pp. 553-569 ◽  
Author(s):  
Amod Jog ◽  
Andrew Hoopes ◽  
Douglas N. Greve ◽  
Koen Van Leemput ◽  
Bruce Fischl
Keyword(s):  
Pulse Sequence ◽  
Brain Segmentation ◽  
Whole Brain

scholarly journals Bayesian QuickNAT: Model uncertainty in deep whole-brain segmentation for structure-wise quality control

NeuroImage ◽  
2019 ◽  
Vol 195 ◽  
pp. 11-22 ◽  
Author(s):  
Abhijit Guha Roy ◽  
Sailesh Conjeti ◽  
Nassir Navab ◽  
Christian Wachinger
Keyword(s):  
Quality Control ◽  
Model Uncertainty ◽  
Brain Segmentation ◽  
Whole Brain

scholarly journals An Intelligent Diagnosis Method of Brain MRI Tumor Segmentation Using Deep Convolutional Neural Network and SVM Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Wentao Wu ◽  
Daning Li ◽  
Jiaoyang Du ◽  
Xiangyu Gao ◽  
Wen Gu ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Brain Tumor ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Support Vector ◽  
Tumor Segmentation ◽  
Brain Segmentation ◽  
Brain Tumor Segmentation ◽  
Segmentation Methods

Among the currently proposed brain segmentation methods, brain tumor segmentation methods based on traditional image processing and machine learning are not ideal enough. Therefore, deep learning-based brain segmentation methods are widely used. In the brain tumor segmentation method based on deep learning, the convolutional network model has a good brain segmentation effect. The deep convolutional network model has the problems of a large number of parameters and large loss of information in the encoding and decoding process. This paper proposes a deep convolutional neural network fusion support vector machine algorithm (DCNN-F-SVM). The proposed brain tumor segmentation model is mainly divided into three stages. In the first stage, a deep convolutional neural network is trained to learn the mapping from image space to tumor marker space. In the second stage, the predicted labels obtained from the deep convolutional neural network training are input into the integrated support vector machine classifier together with the test images. In the third stage, a deep convolutional neural network and an integrated support vector machine are connected in series to train a deep classifier. Run each model on the BraTS dataset and the self-made dataset to segment brain tumors. The segmentation results show that the performance of the proposed model is significantly better than the deep convolutional neural network and the integrated SVM classifier.

scholarly journals Robust whole-brain segmentation: Application to traumatic brain injury

2015 ◽  
Vol 21 (1) ◽  
pp. 40-58 ◽  
Author(s):  
Christian Ledig ◽  
Rolf A. Heckemann ◽  
Alexander Hammers ◽  
Juan Carlos Lopez ◽  
Virginia F.J. Newcombe ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Brain Segmentation ◽  
Whole Brain
Sign in / Sign up

Export Citation Format

Share Document