The Experts below are selected from a list of 10122 Experts worldwide ranked by ideXlab platform
Kai Xiao - One of the best experts on this subject based on the ideXlab platform.
-
Brain MRI Tumor Segmentation with 3D Intracranial Structure Deformation Features
IEEE Intelligent Systems, 2016Co-Authors: Shang-ling Jui, Shichen Zhang, Aboul Ella Hassanien, Weilun Xiong, Dongmei Wang, Kai XiaoAbstract:Extraction of relevant features is of significant importance for brain Tumor Segmentation systems. To improve brain Tumor Segmentation accuracy, the authors present an improved feature extraction component that takes advantage of the correlation between intracranial structure deformation and the compression resulting from brain Tumor growth. Using 3D nonrigid registration and deformation modeling techniques, the component measures lateral ventricular (LaV) deformation in volumetric magnetic resonance images. By verifying the location of the extracted LaV deformation feature data and applying the features on brain Tumor Segmentation with widely used classification algorithms, the authors evaluate the proposed component qualitatively and quantitatively with promising results on 11 datasets comprising real and simulated patient images.
-
AISI - Unsupervised Brain MRI Tumor Segmentation with Deformation-Based Feature
Advances in Intelligent Systems and Computing, 2015Co-Authors: Shichen Zhang, Shang-ling Jui, Aboul Ella Hassanien, Kai XiaoAbstract:Deformation-based features has been proven effective for enhancing brain Tumor Segmentation accuracy. In our previous work, a component for extracting features based on brain lateral ventricular (LaV) deformation has been proposed. By employing the extracted feature on classifiers of artificial neural networks (ANN) and support vector machines (SVM), we have demonstrated its effect for enhancing brain magnetic resonance (MR) image Tumor Segmentation accuracy with supervised Segmentation methods. In this paper, we propose an unsupervised brain Tumor Segmentation system with the use of extracted brain LaV deformation feature. By modifying the LaV deformation feature component, deformation-based feature is combined with MR image features as input dataset for the unsupervised fuzzy c-means (FCM) to perform clustering. Experimental results shows the positive effect from the deformation-based feature on FCM-based unsupervised brain Tumor Segmentation accuracy.
-
ICIG - Brain MR Image Tumor Segmentation with Ventricular Deformation
2011 Sixth International Conference on Image and Graphics, 2011Co-Authors: Kai Xiao, Aboul Ella Hassanien, Yan SunAbstract:This paper addresses the issue of the weak association between brain MRI intensity value and anatomical meaning of MR image pixels. By investigating the deformation on brain lateral ventricles and compression from Tumor, the correlation between them is quantified and utilized. With the proposed feature extraction component, lateral ventricular deformation is transformed into an additional feature for brain Tumor Segmentation. Some comparative experiments using both supervised and unsupervised pattern recognition Segmentation methods show the improved Tumor Segmentation accuracy in some image cases.
-
ICCP - Brain MR image Tumor Segmentation with ventricular deformation
2011 IEEE 7th International Conference on Intelligent Computer Communication and Processing, 2011Co-Authors: Kai Xiao, Aboul Ella Hassanien, Yan SunAbstract:This paper addresses the issue of the weak association between brain MRI intensity value and anatomical meaning of MR image pixels. By investigating the deformation on brain lateral ventricles and compression from Tumor, the correlation between them is quantified and utilized. With the proposed feature extraction component, lateral ventricular deformation is transformed into an additional feature for brain Tumor Segmentation. Some comparative experiments using both supervised and unsupervised pattern recognition Segmentation methods show the improved Tumor Segmentation accuracy in some image cases.
Lin Luo - One of the best experts on this subject based on the ideXlab platform.
-
Brain Tumor Segmentation and Survival Prediction Using Multimodal MRI Scans With Deep Learning
Frontiers in neuroscience, 2019Co-Authors: Li Sun, Songtao Zhang, Hang Chen, Lin LuoAbstract:Gliomas are the most common primary brain malignancies. Accurate and robust Tumor Segmentation and prediction of patients' overall survival are important for diagnosis, treatment planning and risk factor identification. Here we present a deep learning-based framework for brain Tumor Segmentation and survival prediction in glioma, using multimodal MRI scans. For Tumor Segmentation, we use ensembles of three different 3D CNN architectures for robust performance through a majority rule. This approach can effectively reduce model bias and boost performance. For survival prediction, we extract 4,524 radiomic features from segmented Tumor regions, then, a decision tree and cross validation are used to select potent features. Finally, a random forest model is trained to predict the overall survival of patients. The 2018 MICCAI Multimodal Brain Tumor Segmentation Challenge (BraTS), ranks our method at 2nd and 5th place out of 60+ participating teams for survival prediction tasks and Segmentation tasks respectively, achieving a promising 61.0% accuracy on the classification of short-survivors, mid-survivors and long-survivors.
-
BrainLes@MICCAI (2) - Tumor Segmentation and Survival Prediction in Glioma with Deep Learning
Brainlesion: Glioma Multiple Sclerosis Stroke and Traumatic Brain Injuries, 2019Co-Authors: Li Sun, Songtao Zhang, Lin LuoAbstract:Every year, about 238,000 patients are diagnosed with brain Tumor in the world. Accurate and robust Tumor Segmentation and prediction of patients’ overall survival are important for diagnosis, treatment planning and risk factor characterization. Here we present a deep learning-based framework for brain Tumor Segmentation and survival prediction in glioma using multimodal MRI scans. For Tumor Segmentation, we use ensembles of three different 3D CNN architectures for robust performance through majority rule. This approach can effectively reduce model bias and boost performance. For survival prediction, we extract 4524 radiomic features from segmented Tumor region. Then decision tree and cross validation are used to select potent features. Finally, a random forest model is trained to predict the overall survival of patients. On 2018 MICCAI Multimodal Brain Tumor Segmentation Challenge (BraTS), our method ranks at second place and 5th place out of 60+ participating teams on survival prediction task and Segmentation task respectively, achieving a promising 61.0% accuracy on classification of long-survivors, mid-survivors and short-survivors.
Khan M. Iftekharuddin - One of the best experts on this subject based on the ideXlab platform.
-
BrainLes@MICCAI (2) - Multimodal Brain Tumor Segmentation and Survival Prediction Using Hybrid Machine Learning
Brainlesion: Glioma Multiple Sclerosis Stroke and Traumatic Brain Injuries, 2020Co-Authors: Linmin Pei, L. Vidyaratne, Monibor Rahman, Zeina A. Shboul, Khan M. IftekharuddinAbstract:In this paper, we propose a UNet-VAE deep neural network architecture for brain Tumor Segmentation and survival prediction. UNet-VAE architecture has shown great success in brain Tumor Segmentation in the multimodal brain Tumor Segmentation (BraTS) 2018 challenge. In this work, we utilize the UNet-VAE to extract high dimension features, then fuse with hand-crafted texture features to perform survival prediction. We apply the proposed method to the BraTS 2019 validation dataset for both Tumor Segmentation and survival prediction. The Tumor Segmentation result shows dice score coefficient (DSC) of 0.759, 0.90, and 0.806 for enhancing Tumor (ET), whole Tumor (WT), and Tumor core (TC), respectively. For the feature fusion-based survival prediction method, we achieve 56.4% classification accuracy with mean square error (MSE) 101577, and 51.7% accuracy with MSE 70590 for training and validation, respectively. In testing phase, the proposed method for Tumor Segmentation achieves average DSC of 0.81328, 0.88616, and 0.84084 for ET, WT, and TC, respectively. Moreover, the model offers accuracy of 0.439 with MSE of 449009.135 for overall survival prediction in testing phase.
-
Longitudinal brain Tumor Segmentation prediction in MRI using feature and label fusion
Biomedical Signal Processing and Control, 2020Co-Authors: Linmin Pei, Spyridon Bakas, Arastoo Vossough, Syed M. S. Reza, Christos Davatzikos, Khan M. IftekharuddinAbstract:Abstract This work proposes a novel framework for brain Tumor Segmentation prediction in longitudinal multimodal MRI scans, comprising two methods; feature fusion and joint label fusion (JLF). The first method fuses stochastic multi-resolution texture features with Tumor cell density feature to obtain Tumor Segmentation predictions in follow-up timepoints using data from baseline pre-operative timepoint. The cell density feature is obtained by solving the 3D reaction-diffusion equation for biophysical Tumor growth modelling using the Lattice-Boltzmann method. The second method utilizes JLF to combine Segmentation labels obtained from (i) the stochastic texture feature-based and Random Forest (RF)-based Tumor Segmentation method; and (ii) another state-of-the-art Tumor growth and Segmentation method, known as boosted Glioma Image Segmentation and Registration (GLISTRboost, or GB). We quantitatively evaluate both proposed methods using the Dice Similarity Coefficient (DSC) in longitudinal scans of 9 patients from the public BraTS 2015 multi-institutional dataset. The evaluation results for the feature-based fusion method show improved Tumor Segmentation prediction for the whole Tumor(DSCWT = 0.314, p = 0.1502), Tumor core (DSCTC = 0.332, p = 0.0002), and enhancing Tumor (DSCET = 0.448, p = 0.0002) regions. The feature-based fusion shows some improvement on Tumor prediction of longitudinal brain Tumor tracking, whereas the JLF offers statistically significant improvement on the actual Segmentation of WT and ET (DSCWT = 0.85 ± 0.055, DSCET = 0.837 ± 0.074), and also improves the results of GB. The novelty of this work is two-fold: (a) exploit Tumor cell density as a feature to predict brain Tumor Segmentation, using a stochastic multi-resolution RF-based method, and (b) improve the performance of another successful Tumor Segmentation method, GB, by fusing with the RF-based Segmentation labels.
-
Texture Models for Brain Tumor Segmentation
Imaging and Applied Optics, 2013Co-Authors: Khan M. IftekharuddinAbstract:We have been developing multiresolution fractal texture models in multimodal MRI for brain Tumor Segmentation. In this paper, we show statistical efficacy of our models using both our clinical and publicly available brain Tumor datasets.
-
efficacy of texture shape and intensity feature fusion for posterior fossa Tumor Segmentation in mri
International Conference of the IEEE Engineering in Medicine and Biology Society, 2011Co-Authors: Saad Ahmed, Khan M. Iftekharuddin, Arastoo VossoughAbstract:Our previous works suggest that fractal texture feature is useful to detect pediatric brain Tumor in multimodal MRI. In this study, we systematically investigate efficacy of using several different image features such as intensity, fractal texture, and level-set shape in Segmentation of posterior-fossa (PF) Tumor for pediatric patients. We explore effectiveness of using four different feature selection and three different Segmentation techniques, respectively, to discriminate Tumor regions from normal tissue in multimodal brain MRI. We further study the selective fusion of these features for improved PF Tumor Segmentation. Our result suggests that Kullback-Leibler divergence measure for feature ranking and selection and the expectation maximization algorithm for feature fusion and Tumor Segmentation offer the best results for the patient data in this study. We show that for T1 and fluid attenuation inversion recovery (FLAIR) MRI modalities, the best PF Tumor Segmentation is obtained using the texture feature such as multifractional Brownian motion (mBm) while that for T2 MRI is obtained by fusing level-set shape with intensity features. In multimodality fused MRI (T1, T2, and FLAIR), mBm feature offers the best PF Tumor Segmentation performance. We use different similarity metrics to evaluate quality and robustness of these selected features for PF Tumor Segmentation in MRI for ten pediatric patients.
Pilar Sobrevilla - One of the best experts on this subject based on the ideXlab platform.
-
state of the art survey on mri brain Tumor Segmentation
Magnetic Resonance Imaging, 2013Co-Authors: Nelly Gordillo, E Montseny, Pilar SobrevillaAbstract:Brain Tumor Segmentation consists of separating the different Tumor tissues (solid or active Tumor, edema, and necrosis) from normal brain tissues: gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). In brain Tumor studies, the existence of abnormal tissues may be easily detectable most of the time. However, accurate and reproducible Segmentation and characterization of abnormalities are not straightforward. In the past, many researchers in the field of medical imaging and soft computing have made significant survey in the field of brain Tumor Segmentation. Both semiautomatic and fully automatic methods have been proposed. Clinical acceptance of Segmentation techniques has depended on the simplicity of the Segmentation, and the degree of user supervision. Interactive or semiautomatic methods are likely to remain dominant in practice for some time, especially in these applications where erroneous interpretations are unacceptable. This article presents an overview of the most relevant brain Tumor Segmentation methods, conducted after the acquisition of the image. Given the advantages of magnetic resonance imaging over other diagnostic imaging, this survey is focused on MRI brain Tumor Segmentation. Semiautomatic and fully automatic techniques are emphasized.
Li Sun - One of the best experts on this subject based on the ideXlab platform.
-
Brain Tumor Segmentation and Survival Prediction Using Multimodal MRI Scans With Deep Learning
Frontiers in neuroscience, 2019Co-Authors: Li Sun, Songtao Zhang, Hang Chen, Lin LuoAbstract:Gliomas are the most common primary brain malignancies. Accurate and robust Tumor Segmentation and prediction of patients' overall survival are important for diagnosis, treatment planning and risk factor identification. Here we present a deep learning-based framework for brain Tumor Segmentation and survival prediction in glioma, using multimodal MRI scans. For Tumor Segmentation, we use ensembles of three different 3D CNN architectures for robust performance through a majority rule. This approach can effectively reduce model bias and boost performance. For survival prediction, we extract 4,524 radiomic features from segmented Tumor regions, then, a decision tree and cross validation are used to select potent features. Finally, a random forest model is trained to predict the overall survival of patients. The 2018 MICCAI Multimodal Brain Tumor Segmentation Challenge (BraTS), ranks our method at 2nd and 5th place out of 60+ participating teams for survival prediction tasks and Segmentation tasks respectively, achieving a promising 61.0% accuracy on the classification of short-survivors, mid-survivors and long-survivors.
-
BrainLes@MICCAI (2) - Tumor Segmentation and Survival Prediction in Glioma with Deep Learning
Brainlesion: Glioma Multiple Sclerosis Stroke and Traumatic Brain Injuries, 2019Co-Authors: Li Sun, Songtao Zhang, Lin LuoAbstract:Every year, about 238,000 patients are diagnosed with brain Tumor in the world. Accurate and robust Tumor Segmentation and prediction of patients’ overall survival are important for diagnosis, treatment planning and risk factor characterization. Here we present a deep learning-based framework for brain Tumor Segmentation and survival prediction in glioma using multimodal MRI scans. For Tumor Segmentation, we use ensembles of three different 3D CNN architectures for robust performance through majority rule. This approach can effectively reduce model bias and boost performance. For survival prediction, we extract 4524 radiomic features from segmented Tumor region. Then decision tree and cross validation are used to select potent features. Finally, a random forest model is trained to predict the overall survival of patients. On 2018 MICCAI Multimodal Brain Tumor Segmentation Challenge (BraTS), our method ranks at second place and 5th place out of 60+ participating teams on survival prediction task and Segmentation task respectively, achieving a promising 61.0% accuracy on classification of long-survivors, mid-survivors and short-survivors.
