scholarly journals Impact of Spherical Coordinates Transformation Pre-processing in Deep Convolution Neural Networks for Brain Tumor Segmentation and Survival Prediction

2021 ◽  
pp. 295-306 ◽  
Author(s):  
Carlo Russo ◽  
Sidong Liu ◽  
Antonio Di Ieva
Keyword(s):  
Neural Networks ◽  
Brain Tumor ◽  
Tumor Segmentation ◽  
Survival Prediction ◽  
Spherical Coordinates ◽  
Brain Tumor Segmentation ◽  
Convolution Neural Networks ◽  
Coordinates Transformation

Brain Tumor Segmentation Using Deep Neural Networks and Survival Prediction

2021 ◽  
pp. 89-99
Author(s):  
Xiaoxu Na ◽  
Li Ma ◽  
Mariofanna Milanova ◽  
Mary Qu Yang
Keyword(s):  
Neural Networks ◽  
Brain Tumor ◽  
Deep Neural Networks ◽  
Tumor Segmentation ◽  
Survival Prediction ◽  
Brain Tumor Segmentation

VGG-UNET for Brain Tumor Segmentation and Ensemble Model for Survival Prediction

2021 ◽  
Author(s):  
Ali Nawaz ◽  
Usman Akram ◽  
Anum Abdul Salam ◽  
Amad Rizwan Ali ◽  
Attique Ur Rehman ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Tumor Segmentation ◽  
Survival Prediction ◽  
Ensemble Model ◽  
Brain Tumor Segmentation

scholarly journals A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction

2021 ◽  
Author(s):  
Rupal Agravat ◽  
Mehul Raval
Keyword(s):  
Overall Survival ◽  
Brain Tumor ◽  
Automatic Segmentation ◽  
High Volume ◽  
Physical Symptoms ◽  
Tumor Segmentation ◽  
Survival Prediction ◽  
Neural Network Models ◽  
Brain Tumor Segmentation ◽  
Overall Survival Prediction

<div>Glioma is the most deadly brain tumor with high mortality. Treatment planning by human experts depends on the proper diagnosis of physical symptoms along with Magnetic Resonance(MR) image analysis. Highly variability of a brain tumor in terms of size, shape, location, and a high volume of MR images makes the analysis time-consuming. Automatic segmentation methods achieve a reduction in time with excellent reproducible results.</div><div>The article aims to survey the advancement of automated methods for Glioma brain tumor segmentation. It is also essential to make an objective evaluation of various models based on the benchmark. Therefore, the 2012 - 2019 BraTS challenges database evaluates state-of-the-art methods. The complexity of tasks under the challenge has grown from segmentation (Task1) to overall survival prediction (Task 2) to uncertainty prediction for classification (Task 3). The paper covers the complete gamut of brain tumor segmentation using handcrafted features to deep neural network models for Task 1. The aim is to showcase a complete change of trends in automated brain tumor models. The paper also covers end to end joint models involving brain tumor segmentation and overall survival prediction. All the methods are probed, and parameters that affect performance are tabulated and analyzed.</div>

Exploring Graph-Based Neural Networks for Automatic Brain Tumor Segmentation

2021 ◽  
pp. 18-37
Author(s):  
Camillo Saueressig ◽  
Adam Berkley ◽  
Elliot Kang ◽  
Reshma Munbodh ◽  
Ritambhara Singh
Keyword(s):  
Neural Networks ◽  
Brain Tumor ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation
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