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.[Jinchang],
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
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 .