Brain tumor segmentation and overall survival period prediction in glioblastoma multiforme using radiomic features

2021 ◽  
Author(s):  
Suchismita Das ◽  
Srijib Bose ◽  
Gopal K. Nayak ◽  
Suresh Chandra Satapathy ◽  
Sanjay Saxena
Keyword(s):  
Overall Survival ◽  
Brain Tumor ◽  
Glioblastoma Multiforme ◽  
Survival Period ◽  
Tumor Segmentation ◽  
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>

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>

scholarly journals Context aware deep learning for brain tumor segmentation, subtype classification, and survival prediction using radiology images

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Linmin Pei ◽  
Lasitha Vidyaratne ◽  
Md Monibor Rahman ◽  
Khan M. Iftekharuddin
Keyword(s):  
Overall Survival ◽  
Deep Learning ◽  
Brain Tumor ◽  
Tumor Classification ◽  
Tumor Segmentation ◽  
Survival Prediction ◽  
Context Aware ◽  
Brain Tumor Segmentation ◽  
Subtype Classification ◽  
Overall Survival Prediction

AbstractA brain tumor is an uncontrolled growth of cancerous cells in the brain. Accurate segmentation and classification of tumors are critical for subsequent prognosis and treatment planning. This work proposes context aware deep learning for brain tumor segmentation, subtype classification, and overall survival prediction using structural multimodal magnetic resonance images (mMRI). We first propose a 3D context aware deep learning, that considers uncertainty of tumor location in the radiology mMRI image sub-regions, to obtain tumor segmentation. We then apply a regular 3D convolutional neural network (CNN) on the tumor segments to achieve tumor subtype classification. Finally, we perform survival prediction using a hybrid method of deep learning and machine learning. To evaluate the performance, we apply the proposed methods to the Multimodal Brain Tumor Segmentation Challenge 2019 (BraTS 2019) dataset for tumor segmentation and overall survival prediction, and to the dataset of the Computational Precision Medicine Radiology-Pathology (CPM-RadPath) Challenge on Brain Tumor Classification 2019 for tumor classification. We also perform an extensive performance evaluation based on popular evaluation metrics, such as Dice score coefficient, Hausdorff distance at percentile 95 (HD95), classification accuracy, and mean square error. The results suggest that the proposed method offers robust tumor segmentation and survival prediction, respectively. Furthermore, the tumor classification results in this work is ranked at second place in the testing phase of the 2019 CPM-RadPath global challenge.

Brain tumor segmentation and multiview multiscale‐based radiomic model for patient's overall survival prediction

2021 ◽  
Author(s):  
Kiran Fiaz ◽  
Tahir Mustafa Madni ◽  
Fozia Anwar ◽  
Uzair Iqbal Janjua ◽  
Asra Rafi ◽  
...  
Keyword(s):  
Overall Survival ◽  
Brain Tumor ◽  
Tumor Segmentation ◽  
Survival Prediction ◽  
Brain Tumor Segmentation ◽  
Overall Survival Prediction

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>

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