Brain Tumor Segmentation and Survival Prediction

Agravat, Rupal; Raval, Mehul S.

doi:10.1007/978-3-030-46640-4_32

“…Once the tumor is segmented, features are extracted for overall survival prediction. Agravat et al [6] used dense layers U-Net trained on the focal loss for segmentation. Next, age, statistical features and radiomic features train the Random Forest Regressor (RFR) for survival prediction and the obtained accuracy on the test dataset was 0.58.…”

Section: Introductionmentioning

confidence: 99%

Glioblastoma Multiforme Patient Survival Prediction

Rajput¹,

Agravat²,

Roy³

et al. 2021

Preprint

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Glioblastoma Multiforme is a very aggressive type of brain tumor. Due to spatial and temporal intra-tissue inhomogeneity, location and the extent of the cancer tissue, it is difficult to detect and dissect the tumor regions. In this paper, we propose survival prognosis models using four regressors operating on handcrafted image-based and radiomics features. We hypothesize that the radiomics shape features have the highest correlation with survival prediction. The proposed approaches were assessed on the Brain Tumor Segmentation (BraTS-2020) challenge dataset. The highest accuracy of image features with random forest regressor approach was 51.5\% for the training and 51.7\% for the validation dataset. The gradient boosting regressor with shape features gave an accuracy of 91.5\% and 62.1\% on training and validation datasets respectively. It is better than the BraTS 2020 survival prediction challenge winners on the training and validation datasets. Our work shows that handcrafted features exhibit a strong correlation with survival prediction. The consensus based regressor with gradient boosting and radiomics shape features is the best combination for survival prediction.

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“…Tractrographic features from network segmented regions trains SVM classifiers with the linear kernel to predict OS. Authors in [3] implements 2D U-net of three stages with dense blocks at every encoder level, and the feature set of [6] of necrosis tumor sub-region for OS prediction.…”

Section: End To End Methods For Tumor Segmentation and Os Predictionmentioning

confidence: 99%

“…The authors of the article have adopted 2D U-net architecture with three layers [3]. Each encoder layer is replaced with a dense module as shown in Fig 16. In the first phase, the network is trained for whole tumor for 50 epochs with dice loss function.…”

Section: Proposed Architecture For Tumor Segmentationmentioning

confidence: 99%

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

Agravat¹,

Raval²

2021

Preprint

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<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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“…Tractrographic features from network segmented regions trained SVM classifiers with a linear kernel to predict the OS. Authors in [3] implemented a 2D Unet of three stages with dense blocks at every encoder level, and the feature set of [7] of the necrosis tumor sub-region for the OS prediction.…”

Section: End-to-end Methods For Tumor Segmentation and Os Predictionmentioning

confidence: 99%

“…The authors of the article have adopted a 2D U-net architecture with three layers [3]. Each encoder layer is replaced with a dense module as shown in Fig.…”

Section: Proposed Architecture For Tumor Segmentationmentioning

confidence: 99%

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

Agravat¹,

Raval²

2021

Preprint

View full text Add to dashboard Cite

<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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Brain Tumor Segmentation and Survival Prediction

Cited by 52 publications

References 25 publications

Glioblastoma Multiforme Patient Survival Prediction

Glioblastoma Multiforme Patient Survival Prediction

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

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

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