Prediction of Overall Survival of Brain Tumor Patients

Agravat, Rupal; Raval, Mehul S.

doi:10.1109/tencon.2019.8929497

“…The OS prediction used RF with 14 radiomics features selected from various modality images, Laplacian of Gaussian Images and wavelet decomposed images. Authors in [6] had implemented 2D U-net architecture with three stages for tumor segmentation and age, volumetric, and shape features of the whole tumor were used to predict OS.…”

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

confidence: 99%

“…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%

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>

show abstract

“…The OS prediction used RF with 14 radiomics features selected from various modality images, Laplacian of Gaussian Images and wavelet decomposed images. Authors in [7] implemented a 2D U-net architecture with three stages for tumor segmentation and age, volumetric, and shape features of the whole tumor were used to predict the OS.…”

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

confidence: 99%

“…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%

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>

show abstract

“…In addition, the convolutional implementation of a fully connected layer is applied in this architecture thus attaining 40 times acceleration. A CNN 3D architecture extracting patches of 3D voxels with varying brain MRI modalities [5]. 3D voxels are fed into a 4-layered CNN architecture to predict the tissue label of the centre voxel.…”

Section: Related Workmentioning

confidence: 99%

MRI image segmentation using machine learning networks and level set approaches

Almajmaie

¹

,

Raheem

²

,

Mahmood

³

et al. 2022

IJECE

View full text Add to dashboard Cite

<span>The segmented brain tissues from magnetic resonance images (MRI) always pose substantive challenges to the clinical researcher community, especially while making precise estimation of such tissues. In the recent years, advancements in deep learning techniques, more specifically in fully convolution neural networks (FCN) have yielded path breaking results in segmenting brain tumour tissues with pin-point accuracy and precision, much to the relief of clinical physicians and researchers alike. A new hybrid deep learning architecture combining SegNet and U-Net techniques to segment brain tissue is proposed here. Here, a skip connection of the concerned U-Net network was suitably explored. The results indicated optimal multi-scale information generated from the SegNet, which was further exploited to obtain precise tissue boundaries from the brain images. Further, in order to ensure that the segmentation method performed better in conjunction with precisely delineated contours, the output is incorporated as the level set layer in the deep learning network. The proposed method primarily focused on analysing brain tumor segmentation (BraTS) 2017 and BraTS 2018, dedicated datasets dealing with MRI brain tumour. The results clearly indicate better performance in segmenting brain tumours than existing ones.</span>

show abstract

Prediction of Overall Survival of Brain Tumor Patients

Cited by 22 publications

References 23 publications

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

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

MRI image segmentation using machine learning networks and level set approaches

Contact Info

Product

Resources

About