21.9.1.1 Glioma Detection, Analysis, Brain Glioma

Chapter Contents (Back)
Brain. Brain Tumor. Glioma.
See also Brain Tumors, Cortex, Cancer.
See also Survival Analysis, Cancer Survival.

Gooya, A.[Ali], Biros, G., Davatzikos, C.[Christos],
Deformable Registration of Glioma Images Using EM Algorithm and Diffusion Reaction Modeling,
MedImg(30), No. 2, February 2011, pp. 375-390.
IEEE DOI 1102
BibRef

Gooya, A., Pohl, K.M., Bilello, M., Cirillo, L., Biros, G., Melhem, E.R., Davatzikos, C.,
GLISTR: Glioma Image Segmentation and Registration,
MedImg(31), No. 10, October 2012, pp. 1941-1954.
IEEE DOI 1210
BibRef

Rekik, I.[Islem], Allassonnière, S.[Stéphanie], Clatz, O.[Olivier], Geremia, E.[Ezequiel], Stretton, E.[Erin], Delingette, H.[Hervé], Ayache, N.J.[Nicholas J.],
Tumor growth parameters estimation and source localization from a unique time point: Application to low-grade gliomas,
CVIU(117), No. 3, March 2013, pp. 238-249.
Elsevier DOI 1302
Diffusivity ratio; Source estimation; Eikonal equation; Reaction-diffusion glioma growth modeling BibRef

Cordier, N., Delingette, H., Ayache, N.,
A Patch-Based Approach for the Segmentation of Pathologies: Application to Glioma Labelling,
MedImg(35), No. 4, April 2016, pp. 1066-1076.
IEEE DOI 1604
biomedical MRI BibRef

Sivakumar, P., Ganeshkumar, P.,
CANFIS based glioma brain tumor classification and retrieval system for tumor diagnosis,
IJIST(27), No. 2, 2017, pp. 109-117.
DOI Link 1706
brain tumor, CANFIS, classification, retrieval, segmentation BibRef

Gupta, M.[Manu], Rajagopalan, V.[Venkateswaran], Pioro, E.P.[Erik P.], Prabhakar Rao, B.V.V.S.N.,
Volumetric analysis of MR images for glioma classification and their effect on brain tissues,
SIViP(11), No. 7, October 2017, pp. 1337-1345.
Springer DOI 1708
BibRef

Anitha, R., Raja, D.S.S.[D. Siva Sundhara],
Segmentation of glioma tumors using convolutional neural networks,
IJIST(27), No. 4, 2017, pp. 354-360.
DOI Link 1712
brain tumors, classifier, features, glioma, image fusion BibRef

Chen, L., Zhang, H., Lu, J., Thung, K., Aibaidula, A., Liu, L., Chen, S., Jin, L., Wu, J., Wang, Q., Zhou, L., Shen, D.,
Multi-Label Nonlinear Matrix Completion With Transductive Multi-Task Feature Selection for Joint MGMT and IDH1 Status Prediction of Patient With High-Grade Gliomas,
MedImg(37), No. 8, August 2018, pp. 1775-1787.
IEEE DOI 1808
Feature extraction, Tumors, Imaging, Brain, Pathology, Testing, Brain tumor, high-grade glioma, molecular biomarker, matrix completion BibRef

Sasikanth, S., Kumar, S.S.[S. Suresh],
Glioma tumor detection in brain MRI image using ANFIS-based normalized graph cut approach,
IJIST(28), No. 1, 2018, pp. 64-71.
DOI Link 1802
classifier, glioma tumor, graph cut approach, orientation analysis, validation BibRef

Rajagopal, R.,
Glioma brain tumor detection and segmentation using weighting random forest classifier with optimized ant colony features,
IJIST(29), No. 3, September 2019, pp. 353-359.
DOI Link 1908
BibRef

Mahesh, K.M.[K. Michael], Renjit, J.A.[J. Arokia],
Multiclassifier for severity-level categorization of glioma tumors using multimodal magnetic resonance imaging brain images,
IJIST(30), No. 1, 2020, pp. 234-251.
DOI Link 2002
deep convolutional neural networks, Jaya optimization algorithm, multimodal MRI brain images, severity-level classification BibRef

Dogra, J.[Jyotsna], Jain, S.[Shruti], Sood, M.[Meenakshi],
Glioma extraction from MR images employing Gradient Based Kernel Selection Graph Cut technique,
VC(36), No. 5, May 2020, pp. 875-891.
WWW Link. 2005
BibRef

Dogra, J.[Jyotsna], Jain, S.[Shruti], Sood, M.[Meenakshi],
Gradient-based kernel selection technique for tumour detection and extraction of medical images using graph cut,
IET-IPR(14), No. 1, January 2020, pp. 84-93.
DOI Link 1912
BibRef

Mahesh, K.M.[K. Michael], Renjit, J.A.[J. Arokia],
DeepJoint segmentation for the classification of severity-levels of glioma tumour using multimodal MRI images,
IET-IPR(14), No. 11, September 2020, pp. 2541-2552.
DOI Link 2009
BibRef

Peng, S.T.[Su-Ting], Chen, W.[Wei], Sun, J.W.[Jia-Wei], Liu, B.Q.[Bo-Qiang],
Multi-Scale 3D U-Nets: An approach to automatic segmentation of brain tumor,
IJIST(30), No. 1, 2020, pp. 5-17.
DOI Link 2002
brain tumor, CNN, deep learning, gliomas, image segmentation BibRef

Tiwari, A.[Arti], Srivastava, S.[Shilpa], Pant, M.[Millie],
Brain tumor segmentation and classification from magnetic resonance images: Review of selected methods from 2014 to 2019,
PRL(131), 2020, pp. 244-260.
Elsevier DOI 2004
Brain tumor segmentation, Glioma, Neoplasia, Magnetic resonance imaging (MRI) BibRef

Zhang, W.X.[Wen-Xue], Jian, J.B.[Jian-Bo], Sun, C.[Cuiyun], Chen, J.[Jie], Lv, W.J.[Wen-Juan], Sun, M.Y.[Meng-Yu], Zhao, Y.Q.[Yu-Qing], Zhao, Q.[Qi], Hu, C.H.[Chun-Hong],
High-resolution 3D imaging of microvascular architecture in human glioma tissues using X-ray phase-contrast computed tomography as a potential adjunct to histopathology,
IJIST(30), No. 2, 2020, pp. 464-472.
DOI Link 2005
glioma, microthrombi, microvascular architecture, X-ray phase-contrast computed tomography BibRef

Lu, Z.Y.[Zhen-Yu], Bai, Y.Z.[Yan-Zhong], Chen, Y.[Yi], Su, C.Q.[Chun-Qiu], Lu, S.S.[Shan-Shan], Zhan, T.M.[Tian-Ming], Hong, X.N.[Xun-Ning], Wang, S.H.[Shui-Hua],
The classification of gliomas based on a Pyramid dilated convolution resnet model,
PRL(133), 2020, pp. 173-179.
Elsevier DOI 2005
Gliomas, Classification, Deep learning, ResNet, Dilated convolution BibRef

Kalaiselvi, T.[Thiruvenkadam], Padmapriya, T.[Thiyagarajan], Sriramakrishnan, P.[Padmanaban], Priyadharshini, V.[Venugopal],
Development of automatic glioma brain tumor detection system using deep convolutional neural networks,
IJIST(30), No. 4, 2020, pp. 926-938.
DOI Link 2011
BraTS, deep learning, glioma tumor, neural networks, tumor detection, WBA BibRef

Mohamed, L.A.[Linda Ait], Cherfa, A.[Assia], Cherfa, Y.[Yazid], Belkhamsa, N.[Noureddine], Alim-Ferhat, F.[Fatiha],
Hybrid method combining superpixel, supervised learning, and random walk for glioma segmentation,
IJIST(31), No. 1, 2021, pp. 288-301.
DOI Link 2102
glioma tumor, random forest, random walk, segmentation, superpixels BibRef

Singh, R.[Rahul], Goel, A.[Aditya], Raghuvanshi, D.K.,
Ensemble-based glioma grade classification using Gabor filter bank and rotation forest,
IET-IPR(14), No. 15, 15 December 2020, pp. 3851-3858.
DOI Link 2103
BibRef

Lerche, C.W.[Christoph W.], Radomski, T.[Timon], Lohmann, P.[Philipp], Caldeira, L.[Liliana], Brambilla, C.R.[Cláudia Régio], Tellmann, L.[Lutz], Scheins, J.[Jürgen], Kops, E.R.[Elena Rota], Galldiks, N.[Norbert], Langen, K.J.[Karl-Josef], Herzog, H.[Hans], Shah, N.J.[N. Jon],
A Linearized Fit Model for Robust Shape Parameterization of FET-PET TACs,
MedImg(40), No. 7, July 2021, pp. 1852-1862.
IEEE DOI 2107
Neuroscience, Kinetic theory, Tumors, Brain modeling, Shape, Imaging, Field effect transistors, FET PET, glioma classification, parametric imaging BibRef

Mathiyalagan, G.[Gomathi], Devaraj, D.[Dhanasekaran],
A machine learning classification approach based glioma brain tumor detection,
IJIST(31), No. 3, 2021, pp. 1424-1436.
DOI Link 2108
features, fuzzy logic, glioma, ridgelet filter, tumor BibRef

Barzegar, Z.[Zeynab], Jamzad, M.[Mansour],
Fully automated glioma tumour segmentation using anatomical symmetry plane detection in multimodal brain MRI,
IET-CV(15), No. 7, 2021, pp. 463-473.
DOI Link 2109
BibRef

Singh, R.[Rahul], Goel, A.[Aditya], Raghuvanshi, D.K.[Deepak Kumar],
Binary glioma grading framework employing locality preserving projections and Gaussian radial basis function support vector machine,
IJIST(31), No. 4, 2021, pp. 2047-2059.
DOI Link 2112
Gabor filter bank, Gaussian radial basis function-support vector machine (GRBF-SVM), synthetic minority over-sampling technique (SMOTE) BibRef

Liu, Z.H.[Zhi-Hua], Tong, L.[Lei], Chen, L.[Long], Zhou, F.X.[Fei-Xiang], Jiang, Z.H.[Zhe-Heng], Zhang, Q.[Qianni], Wang, Y.H.[Yin-Hai], Shan, C.F.[Cai-Feng], Li, L.[Ling], Zhou, H.Y.[Hui-Yu],
CANet: Context Aware Network for Brain Glioma Segmentation,
MedImg(40), No. 7, July 2021, pp. 1763-1777.
IEEE DOI 2107
Image segmentation, Tumors, Graph neural networks, Feature extraction, image segmentation BibRef

Boudrioua, A.[Asma], Aloui, A.[Abdelouahab], Solaiman, B.[Basel], Asli, L.[Larbi], Ben Salem, D.[Douraied], Tliba, S.[Souhil],
Automatic three-dimensional detection and volume estimation of low-grade gliomas,
IJIST(31), No. 3, 2021, pp. 1678-1691.
DOI Link 2108
bounding box, edge indicator term, fuzzy preference optimization model, level set method, segmentation BibRef

Hedyehzadeh, M.[Mohammadreza], Maghooli, K.[Keivan], MomenGharibvand, M.[Mohammad],
Glioma grade detection using grasshopper optimization algorithm-optimized machine learning methods: The Cancer Imaging Archive study,
IJIST(31), No. 3, 2021, pp. 1670-1677.
DOI Link 2108
fuzzy C-means, GLCM, glioma grade, grasshopper optimization algorithm, local binary pattern BibRef

Abo Elenein, N.M.[Nagwa M.], Piao, S.H.[Song-Hao], Noor, A.[Alam], Ahmed, P.N.[Pir Noman],
MIRAU-Net: An improved neural network based on U-Net for gliomas segmentation,
SP:IC(101), 2022, pp. 116553.
Elsevier DOI 2201
Brain tumor segmentation, U-Net, Full convolutional network, Inception, Residual Module, Attention Gate BibRef

Jiang, L.Q.[Lin-Qi], Ning, C.Y.[Chun-Yu], Li, J.Y.[Jing-Yang],
Glioma classification framework based on SE-ResNeXt network and its optimization,
IET-IPR(16), No. 2, 2022, pp. 596-605.
DOI Link 2201
BibRef

Fang, Y.[Ying], Huang, H.[He], Yang, W.J.[Wei-Ji], Xu, X.M.[Xiao-Mei], Jiang, W.W.[Wei-Wei], Lai, X.B.[Xiao-Bo],
Nonlocal convolutional block attention module VNet for gliomas automatic segmentation,
IJIST(32), No. 2, 2022, pp. 528-543.
DOI Link 2203
automatic segmentation, convolutional block attention module, deep learning, glioma, nonlocal block BibRef

Paul, R.[Rahul],
Topological features in addition to radiomics signature predict 1P19Q status and tumor grade in low-grade gliomas,
IJIST(32), No. 3, 2022, pp. 753-766.
DOI Link 2205
1p19q codeletion, low grade glioma, radiomics, topological data analysis, topological features, tumor grade BibRef

Cheng, J.H.[Jian-Hong], Liu, J.[Jin], Kuang, H.[Hulin], Wang, J.X.[Jian-Xin],
A Fully Automated Multimodal MRI-Based Multi-Task Learning for Glioma Segmentation and IDH Genotyping,
MedImg(41), No. 6, June 2022, pp. 1520-1532.
IEEE DOI 2206
Image segmentation, Multitasking, Task analysis, Magnetic resonance imaging, Feature extraction, Tumors, transformer BibRef

Gore, S.[Sonal], Jagtap, J.[Jayant],
IDH-Based Radiogenomic Characterization of Glioma Using Local Ternary Pattern Descriptor Integrated with Radiographic Features and Random Forest Classifier,
IJIG(22), No. 3 2022, pp. 2140013.
DOI Link 2206
BibRef

Xiao, A.[Anqi], Shen, B.[Biluo], Shi, X.J.[Xiao-Jing], Zhang, Z.[Zhe], Zhang, Z.[Zeyu], Tian, J.[Jie], Ji, N.[Nan], Hu, Z.H.[Zhen-Hua],
Intraoperative Glioma Grading Using Neural Architecture Search and Multi-Modal Imaging,
MedImg(41), No. 10, October 2022, pp. 2570-2581.
IEEE DOI 2210
Imaging, Fluorescence, Feature extraction, Surgery, Biomedical imaging, Medical diagnostic imaging, Deep learning, NIR-II fluorescence imaging BibRef

Hussain, S.S.[Syed Sajid], Sachdeva, J.[Jainy], Ahuja, C.K.[Chirag Kamal], Singh, A.[Abhiav],
Enc-Unet: A novel method for Glioma segmentation,
IJIST(33), No. 2, 2023, pp. 465-482.
DOI Link 2303
3D-Unet, autoencoders, deep learning, glioma segmentation, MRI BibRef

Gudigar, A.[Anjan], Raghavendra, U., Rao, T.N.[Tejaswi N.], Samanth, J.[Jyothi], Rajinikanth, V.[Venkatesan], Satapathy, S.C.[Suresh Chandra], Ciaccio, E.J.[Edward J.], Yee, C.W.[Chan Wai], Acharya, U.R.[U. Rajendra],
FFCAEs: An efficient feature fusion framework using cascaded autoencoders for the identification of gliomas,
IJIST(33), No. 2, 2023, pp. 483-494.
DOI Link 2303
cascaded autoencoders, classification accuracy, computer-aided diagnostic tool, feature fusion framework, magnetic resonance (MR) BibRef

Malhotra, R.[Radhika], Singh-Saini, B.[Barjinder], Gupta, S.[Savita],
CB-D2RNet: An efficient context bridge network for glioma segmentation,
JVCIR(94), 2023, pp. 103836.
Elsevier DOI 2306
MRI, Context bridge, Loss function, Dilated convolution BibRef

Akram, M.T.[Muhammad Tahir], Asghar, S.[Sohail], Shahid, A.R.[Ahmad Raza],
Effective data augmentation for brain tumor segmentation,
IJIST(33), No. 4, 2023, pp. 1247-1260.
DOI Link 2307
2D U-Nets, brain tumor segmentation, data augmentation, glioma segmentation BibRef

Liang, B.S.[Bao-Shan], Tan, J.Y.[Jing-Ye], Lozenski, L.[Luke], Hormuth, D.A.[David A.], Yankeelov, T.E.[Thomas E.], Villa, U.[Umberto], Faghihi, D.[Danial],
Bayesian Inference of Tissue Heterogeneity for Individualized Prediction of Glioma Growth,
MedImg(42), No. 10, October 2023, pp. 2865-2875.
IEEE DOI 2310
BibRef

Ayadi, W.[Wadhah], Elhamzi, W.[Wajdi], Atri, M.[Mohamed],
A deep conventional neural network model for glioma tumor segmentation,
IJIST(33), No. 5, 2023, pp. 1593-1605.
DOI Link 2310
brain, BraTS, CNN, medical diagnosis, MRI, segmentation, tumor BibRef

Yin, Z.M.[Zi-Ming], Gao, H.Y.[Hong-Yu], Gong, J.C.[Jin-Chang], Wang, Y.J.[Yuan-Jun],
WD-UNeXt: Weight loss function and dropout U-Net with ConvNeXt for automatic segmentation of few shot brain gliomas,
IET-IPR(17), No. 11, 2023, pp. 3271-3280.
DOI Link 2310
brain gliomas, ConvNeXt, deep learning, MRI, U-Net BibRef

Wei, Y.[Yiran], Chen, X.[Xi], Zhu, L.[Lei], Zhang, L.[Lipei], Schönlieb, C.B.[Carola-Bibiane], Price, S.[Stephen], Li, C.[Chao],
Multi-Modal Learning for Predicting the Genotype of Glioma,
MedImg(42), No. 11, November 2023, pp. 3167-3178.
IEEE DOI 2311
BibRef

Wang, H.F.[Hong-Fei], Peng, X.[Xinhao], Ma, S.Q.[Shi-Qing], Wang, S.[Shuai], Xu, C.[Chuan], Yang, P.[Ping],
An adaptive enhancement method based on stochastic parallel gradient descent of glioma image,
IET-IPR(17), No. 14, 2023, pp. 3976-3985.
DOI Link 2312
image denoising, image enhancement, parallel algorithms, artefacts suppression, contrast improvement, stochastic parallel gradient descent BibRef

van Garderen, K.A.[Karin A.], van der Voort, S.R.[Sebastian R.], Wijnenga, M.M.J.[Maarten M. J.], Incekara, F.[Fatih], Alafandi, A.[Ahmad], Kapsas, G.[Georgios], Gahrmann, R.[Renske], Schouten, J.W.[Joost W.], Dubbink, H.J.[Hendrikus J.], Vincent, A.J.P.E.[Arnaud J. P. E.], van den Bent, M.[Martin], French, P.J.[Pim J.], Smits, M.[Marion], Klein, S.[Stefan],
Evaluating the Predictive Value of Glioma Growth Models for Low-Grade Glioma After Tumor Resection,
MedImg(43), No. 1, January 2024, pp. 253-263.
IEEE DOI 2401
BibRef

Karun, B.[Baiju], Prasath, T.A.[T. Arun], Rajasekaran, M.P.[M. Pallikonda], Makreri, R.[Rakhee],
Glioma detection using EHO based FLAME clustering in MR brain images,
IJIST(34), No. 1, 2024, pp. e22937.
DOI Link 2401
BraTS dataset, deep learning (DL), elephant herding optimization (EHO), tumour segmentation BibRef


Alksas, A.[Ahmed], Shehata, M.[Mohamed], Atef, H.[Hala], Sherif, F.[Fatma], Yaghi, M.[Maha], Alhalabi, M.[Marah], Ghazal, M.[Mohammed], El Serougy, L.[Lamiaa], El-Baz, A.[Ayman],
A Comprehensive Non-invasive System for Early Grading of Gliomas,
ICPR22(4371-4377)
IEEE DOI 2212
Histograms, Solid modeling, Sensitivity, Imaging, Magnetic resonance, Artificial neural networks, Feature extraction, CAD, MRIs, HOG, GLCM, ADC BibRef

Dequidt, P.[Paul], Bourdon, P.[Pascal], Tremblais, B.[Benoit], Guillevin, C.[Carole], Gianelli, B.[Benoit], Boutet, C.[Claire], Cottier, J.P.[Jean-Philippe], Vallée, J.N.[Jean-Noël], Fernandez-Maloigne, C.[Christine], Guillevin, R.[Rémy],
Assigning a new glioma grade label ground-truth for the BraTS dataset using radiologic criteria,
IPTA20(1-6)
IEEE DOI 2206
Sociology, Magnetic resonance, Organizations, Tools, Oncology, Statistics, Tumors, Glioma grading, machine learning, radiomics BibRef

Brzenczek, C.[Cyril], Mézières, S.[Sophie], Gaudeau, Y.[Yann], Blonski, M.[Marie], Rech, F.[Fabien], Obara, T.[Tiphaine], Taillandier, L.[Luc], Moureaux, J.M.[Jean-Marie],
An original MRI-based method to quantify the Diffuse Low-Grade Glioma brain infiltration,
IPTA20(1-6)
IEEE DOI 2206
Magnetic resonance imaging, Standards organizations, Morphology, Medical treatment, Organizations, Tools, Tumors, Brain, Segmentation BibRef

Li, X.[Xuhui], Xu, Y.[Yong], Xiang, F.[Feng], Liu, Q.[Qing], Huang, W.H.[Wei-Hong], Xie, B.[Bin],
KINET: A Non-Invasive Method for Predicting Ki67 Index of Glioma,
ICIP21(150-154)
IEEE DOI 2201
Deep learning, Training, Magnetic resonance imaging, Surgery, Medical services, Metadata, Ki67 index, KiNet, multi-modal, glioma, MRI BibRef

Ali, M.B.[Muhaddisa Barat], Gu, I.Y.H.[Irene Yu-Hua], Jakola, A.S.[Asgeir Store],
Multi-stream Convolutional Autoencoder and 2D Generative Adversarial Network for Glioma Classification,
CAIP19(I:234-245).
Springer DOI 1909
BibRef

Ge, C.J.[Chen-Jie], Gu, I.Y.H.[Irene Yu-Hua], Jakola, A.S.[Asgeir Store], Yang, J.[Jie],
Cross-Modality Augmentation of Brain MR Images Using a Novel Pairwise Generative Adversarial Network for Enhanced Glioma Classification,
ICIP19(559-563)
IEEE DOI 1910
Pairwise generative adversarial network (GAN), cross-modality image augmentation, glioma classification, MR images BibRef

Naudin, M.[Mathieu], Tremblais, B.[Benoit], Guillevin, C.[Carole], Guillevin, R.[Rémy], Fernandez-Maloigne, C.[Christine],
Diffuse Low Grade Glioma NMR Assessment for Better Intra-operative Targeting Using Fuzzy Logic,
ACIVS18(200-210).
Springer DOI 1810
BibRef

Ge, C., Qu, Q., Gu, I.Y., Store Jakola, A.,
3D Multi-Scale Convolutional Networks for Glioma Grading Using MR Images,
ICIP18(141-145)
IEEE DOI 1809
Tumors, Magnetic resonance imaging, Training, Feature extraction, Machine learning, MRIs BibRef

Murthy, V., Hou, L., Samaras, D., Kurc, T.M., Saltz, J.H.,
Center-Focusing Multi-task CNN with Injected Features for Classification of Glioma Nuclear Images,
WACV17(834-841)
IEEE DOI 1609
Decision support systems, Handheld, computers BibRef

Swiderska, Z.[Zaneta], Markiewicz, T.[Tomasz], Grala, B.[Bartlomiej], Kozlowski, W.[Wojciech],
Texture and Mathematical Morphology for Hot-Spot Detection in Whole Slide Images of Meningiomas and Oligodendrogliomas,
CAIP15(II:1-12).
Springer DOI 1511
BibRef

Dvorak, P.[Pavel], Bartusek, K.[Karel],
Fully Automatic 3D Glioma Extraction in Multi-contrast MRI,
ICIAR14(II: 239-246).
Springer DOI 1410
BibRef

Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Brain, Cortex, Dementia .


Last update:Jul 13, 2024 at 15:27:21