Two-Step U-Nets for Brain Tumor Segmentation and Random Forest with Radiomics for Survival Time Prediction

2020 ◽  
pp. 200-209
Author(s):  
Soopil Kim ◽  
Miguel Luna ◽  
Philip Chikontwe ◽  
Sang Hyun Park
Keyword(s):  
Brain Tumor ◽  
Random Forest ◽  
Survival Time ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Time Prediction

Automatic Brain Tumor Segmentation in Multispectral MRI Volumes Using a Random Forest Approach

2018 ◽  
pp. 137-149
Author(s):  
Zoltán Kapás ◽  
László Lefkovits ◽  
David Iclănzan ◽  
Ágnes Győrfi ◽  
Barna László Iantovics ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Random Forest ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Multispectral Mri

scholarly journals Overall Survival Prediction for Gliomas Using a Novel Compound Approach

2021 ◽  
Vol 11 ◽  
Author(s):  
He Huang ◽  
Wenbo Zhang ◽  
Ying Fang ◽  
Jialing Hong ◽  
Shuaixi Su ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Survival Time ◽  
Tumor Grade ◽  
Tumor Segmentation ◽  
Survival Prediction ◽  
Brain Tumor Segmentation ◽  
Incidence And Mortality ◽  
Treatment Plans ◽  
Magnetic Resonance Imaging Mri

As a highly malignant tumor, the incidence and mortality of glioma are not optimistic. Predicting the survival time of patients with glioma by extracting the feature information from gliomas is beneficial for doctors to develop more targeted treatments. Magnetic resonance imaging (MRI) is a way to quickly and clearly capture the details of brain tissue. However, manually segmenting brain tumors from MRI will cost doctors a lot of energy, and doctors can only vaguely estimate the survival time of glioma patients, which are not conducive to the formulation of treatment plans. Therefore, automatically segmenting brain tumors and accurately predicting survival time has important significance. In this article, we first propose the NLSE-VNet model, which integrates the Non-Local module and the Squeeze-and-Excitation module into V-Net to segment three brain tumor sub-regions in multimodal MRI. Then extract the intensity, texture, wavelet, shape and other radiological features from the tumor area, and use the CNN network to extract the deep features. The factor analysis method is used to reduce the dimensionality of features, and finally the dimensionality-reduced features and clinical features such as age and tumor grade are combined into the random forest regression model to predict survival. We evaluate the effect on the BraTS 2019 and BraTS 2020 datasets. The average Dice of brain tumor segmentation tasks up to 79% and the average RMSE of the survival predictive task is as low as 311.5. The results indicate that the method in this paper has great advantages in segmentation and survival prediction of gliomas.

Recent advancements in Fuzzy C-means based techniques for brain MRI Segmentation

2021 ◽  
Vol 16 ◽  
Author(s):  
Ghazanfar Latif ◽  
Jaafar Alghazo ◽  
Fadi N. Sibai ◽  
D.N.F. Awang Iskandar ◽  
Adil H. Khan
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Brain Mri ◽  
Tumor Segmentation ◽  
Brain Anatomy ◽  
Mri Segmentation ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Low Contrast ◽  
Fuzzy C Means

Background: Variations of image segmentation techniques, particularly those used for Brain MRI segmentation, vary in complexity from basic standard Fuzzy C-means (FCM) to more complex and enhanced FCM techniques. Objective: In this paper, a comprehensive review is presented on all thirteen variations of FCM segmentation techniques. In the review process, the concentration is on the use of FCM segmentation techniques for brain tumors. Brain tumor segmentation is a vital step in the process of automatically diagnosing brain tumors. Unlike segmentation of other types of images, brain tumor segmentation is a very challenging task due to the variations in brain anatomy. The low contrast of brain images further complicates this process. Early diagnosis of brain tumors is indeed beneficial to patients, doctors, and medical providers. Results: FCM segmentation works on images obtained from magnetic resonance imaging (MRI) scanners, requiring minor modifications to hospital operations to early diagnose tumors as most, if not all, hospitals rely on MRI machines for brain imaging. In this paper, we critically review and summarize FCM based techniques for brain MRI segmentation.

Brain Tumor Segmentation Using Deep Belief Networks and Pathological Knowledge

2017 ◽  
Vol 16 (2) ◽  
pp. 129-136 ◽  
Author(s):  
Tianming Zhan ◽  
Yi Chen ◽  
Xunning Hong ◽  
Zhenyu Lu ◽  
Yunjie Chen
Keyword(s):  
Brain Tumor ◽  
Tumor Segmentation ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Brain Tumor Segmentation
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