State of the art survey on MRI brain tumor segmentation
Open accessAbstract
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.
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Most detailed work in this area has depended on novel techniques for brain tumor detection, segmentation, and classification using deep learning and federated learning.
“Most of the detailed work in this area has depended on novel techniques used for brain tumor detection, segmentation, and classification using deep learning and federated learning [8].”
From A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis (2022) · cites this paper as [8]
Cites
Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations
Osher, S., & Sethian, J. A. (1988) · Journal of Computational Physics · doi:10.1016/0021-9991(88)90002-2 · Cited by 14,196 · see citing works
Chan, T. F.-C., & Vese, L. A. (2001) · IEEE Transactions on Image Processing · doi:10.1109/83.902291 · Cited by 10,279 · see citing works
Shape modeling with front propagation: a level set approach
Malladi, R., Sethian, J. A., & Vemuri, B. C. (1995) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.368173 · Cited by 3,166 · see citing works
Adams, R., & Bischof, L. (1994) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.295913 · Cited by 3,155 · see citing works
Current Methods in Medical Image Segmentation
Pham, D. L., Xu, C., & Prince, J. L. (2000) · Annual Review of Biomedical Engineering · doi:10.1146/annurev.bioeng.2.1.315 · Cited by 2,352 · see citing works
A geometric model for active contours in image processing
Caselles, V., Catt�, F., Coll, T., & Dibos, F. (1993) · Numerische Mathematik · doi:10.1007/bf01385685 · Cited by 1,744 · see citing works
Minimization of Region-Scalable Fitting Energy for Image Segmentation
Li, C., Kao, C., Gore, J. C., & Ding, Z. (2008) · IEEE Transactions on Image Processing · doi:10.1109/tip.2008.2002304 · Cited by 1,723 · see citing works
Li, C., Huang, R., Ding, Z., Gatenby, J. C., Metaxas, D., & Gore, J. C. (2011) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2146190 · Cited by 1,286 · see citing works
Chen, S., & Zhang, D. D. (2004) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2004.831165 · Cited by 1,146 · see citing works
Cai, W., Chen, S., & Zhang, D. (2006) · Pattern Recognition · doi:10.1016/j.patcog.2006.07.011 · Cited by 1,089 · see citing works
Review of MR image segmentation techniques using pattern recognition
Bezdek, J. C., Hall, L., & Clarke, L. P. (1993) · Medical Physics · doi:10.1118/1.597000 · Cited by 956 · see citing works
A Review of Methods for Correction of Intensity Inhomogeneity in MRI
Vovk, U., Pernuš, F., & Likar, B. (2007) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2006.891486 · Cited by 852 · see citing works
Gradient flows and geometric active contour models
Kichenassamy, S., Kumar, A., Olver, P. J., et al. (2002) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1995.466855 · Cited by 661 · see citing works
A brain tumor segmentation framework based on outlier detection*1
Prastawa, M. W. (2004) · Medical Image Analysis · doi:10.1016/j.media.2004.06.007 · Cited by 637 · see citing works
A geometric snake model for segmentation of medical imagery
Yezzi, A. J., Kichenassamy, S., Kumar, A., Olver, P. J., & Tannenbaum, A. (1997) · IEEE Transactions on Medical Imaging · doi:10.1109/42.563665 · Cited by 581 · see citing works
McInerney, T., & Terzopoulos, D. (2000) · Cited by 495 · see citing works
Automatic tumor segmentation using knowledge-based techniques
Clark, M. C., Hall, L., Goldgof, D. B., Velthuizen, R. P., Murtagh, F. R., & Silbiger, M. S. (1998) · IEEE Transactions on Medical Imaging · doi:10.1109/42.700731 · Cited by 461 · see citing works
Efficient Multilevel Brain Tumor Segmentation With Integrated Bayesian Model Classification
Corso, J. J., Sharon, E., Dube, S., El-Saden, S. M., Sinha, U. S., & Yuille, A. (2008) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2007.912817 · Cited by 444 · see citing works
MR brain image segmentation using an enhanced fuzzy C-means algorithm
Szilágyi, L. S., Benyó, Z., Szilágyi, S. M., et al. (2004) · doi:10.1109/iembs.2003.1279866 · Cited by 439 · see citing works
Automated Segmentation of MR Images of Brain Tumors
Kaus, M. R., Warfield, S. K., Nabavi, A., Black, P. M., Jolesz, F. A., & Kikinis, R. (2001) · Radiology · doi:10.1148/radiology.218.2.r01fe44586 · Cited by 424 · see citing works
Handbook of Medical Imaging. Processing and Analysis
Bankman, I. N., & Morcovescu, Ş. (2002) · Medical Physics · doi:10.1118/1.1429630 · Cited by 396 · see citing works
Interaction in the segmentation of medical images: A survey
Olabarriaga, S. D., & Smeulders, A. W. M. (2001) · Medical Image Analysis · doi:10.1016/s1361-8415(00)00041-4 · Cited by 385 · see citing works
Watershed-Based Segmentation and Region Merging
Bleau, A., & Leon, L. J. (2000) · Computer Vision and Image Understanding · doi:10.1006/cviu.1999.0822 · Cited by 311 · see citing works
Automatic segmentation of non-enhancing brain tumors in magnetic resonance images
Fletcher-Heath, L., Hall, L., Goldgof, D. B., & Murtagh, F. R. (2001) · Artificial Intelligence in Medicine · doi:10.1016/s0933-3657(00)00073-7 · Cited by 294 · see citing works
Reddick, W. E., Glass, J. O., Cook, E. N., et al. (1997) · IEEE Transactions on Medical Imaging · doi:10.1109/42.650887 · Cited by 293 · see citing works
Khotanlou, H., Colliot, O., Jamal Atif, C., & Isabelle Bloch, A. (2008) · Fuzzy Sets and Systems · doi:10.1016/j.fss.2008.11.016 · Cited by 261 · see citing works
Area and length minimizing flows for shape segmentation
Siddiqi, K., Lauziere, Y. B., Tannenbaum, A., et al. (1998) · IEEE Transactions on Image Processing · doi:10.1109/83.661193 · Cited by 261 · see citing works
Level-set evolution with region competition: automatic 3-D segmentation of brain tumors
Ho, S., Bullitt, E., & Gerig, G. (2003) · doi:10.1109/icpr.2002.1044788 · Cited by 259 · see citing works
Mazzara, G. P., Velthuizen, R. P., Pearlman, J. L., Greenberg, H. M., & Wagner, H. (2004) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2004.01.026 · Cited by 216 · see citing works
Neural-network-based segmentation of multi-modal medical images: a comparative and prospective study
Özkan, M., Dawant, B. M., & Maciunas, R. J. (1993) · IEEE Transactions on Medical Imaging · doi:10.1109/42.241881 · Cited by 213 · see citing works
Automatic brain tumor segmentation by subject specific modification of atlas priors1
Prastawa, M. W., Bullitt, E., Moon, N., Leemput, K. V., & Gerig, G. (2003) · Academic Radiology · doi:10.1016/s1076-6332(03)00506-3 · Cited by 209 · see citing works
Brain tumor characterization using the soft computing technique of fuzzy cognitive maps
Papageorgiou, E. I., Σπυρίδωνος, Π. Π., Glotsos, D. T., Stylios, C. D., Ravazoula, P., Nikiforidis, G., & Groumpos, P. P. (2007) · Applied Soft Computing · doi:10.1016/j.asoc.2007.06.006 · Cited by 206 · see citing works
A Review on MR Image Intensity Inhomogeneity Correction
Hou, Z. (2006) · International Journal of Biomedical Imaging · doi:10.1155/ijbi/2006/49515 · Cited by 201 · see citing works
A framework of fuzzy information fusion for the segmentation of brain tumor tissues on MR images
Dou, W., Ruan, S., Chen, Y., et al. (2006) · Image and Vision Computing · doi:10.1016/j.imavis.2006.01.025 · Cited by 175 · see citing works
Tumour volume determination from MR images by morphological segmentation
Gibbs, P., Buckley, D. L., Blackband, S. J., et al. (1996) · Physics in Medicine and Biology · doi:10.1088/0031-9155/41/11/014 · Cited by 171 · see citing works
Skull-stripping magnetic resonance brain images using a model-based level set
Zhuang, A. H., Valentino, D. J., & Toga, A. W. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.03.019 · Cited by 157 · see citing works
Phillips, W. E., Velthuizen, R. P., Phuphanich, S., et al. (1995) · Magnetic Resonance Imaging · doi:10.1016/0730-725x(94)00093-i · Cited by 150 · see citing works
Fractal-based brain tumor detection in multimodal MRI
Iftekharuddin, K. M., Zheng, J., Islam, M. A., et al. (2008) · Applied Mathematics and Computation · doi:10.1016/j.amc.2007.10.063 · Cited by 146 · see citing works
FUZZY and NEURO-FUZZY SYSTEMS in MEDICINE
Teodorescu, H.-N., Kandel, A., & Jain, L. C. (2017) · CRC Press eBooks · doi:10.1201/9780203713419 · Cited by 133 · see citing works
Stadlbauer, A., Moser, E., Gruber, S., et al. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.06.022 · Cited by 131 · see citing works
Segmenting Brain Tumors with Conditional Random Fields and Support Vector Machines
Lee, C., Schmidt, M., Murtha, A. D., Bistritz, A., Sander, J., & Greiner, R. (2005) · Lecture notes in computer science · doi:10.1007/11569541_47 · Cited by 125 · see citing works
Handbook of Medical Imaging, Processing and Analysis
Fenster, A. (2001) · Ultrasound in Medicine & Biology · doi:10.1016/s0301-5629(01)00360-x · Cited by 122 · see citing works
Semi-automated brain tumor and edema segmentation using MRI
Xie, K., Yang, J., Zhang, Z. G., et al. (2005) · European Journal of Radiology · doi:10.1016/j.ejrad.2005.03.028 · Cited by 120 · see citing works
Interplay between intensity standardization and inhomogeneity correction in MR image processing
Madabhushi, A., & Udupa, J. K. (2005) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2004.843256 · Cited by 116 · see citing works
Forouzanfar, M., Forghani, N., & Teshnehlab, M. (2009) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2009.10.002 · Cited by 113 · see citing works
Siyal, M. Y., & Yu, L. (2005) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.03.019 · Cited by 113 · see citing works
Tumor segmentation from magnetic resonance imaging by learning via one-class support vector machine
Zhang, J., Ma, K., Er, M. H., & Chong, V. F. H. (2004) · Cited by 109 · see citing works
3D Variational Brain Tumor Segmentation using a High Dimensional Feature Set
Cobzaş, D., Birkbeck, N., Schmidt, M., Jägersand, M., & Murtha, A. D. (2007) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4409130 · Cited by 107 · see citing works
Clustering multispectral images: a tutorial
Tran, T. N., Wehrens, R., & Buydens, L. M. C. (2004) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2004.07.011 · Cited by 99 · see citing works
Recognizing Deviations from Normalcy for Brain Tumor Segmentation
Gering, D. T., Grimson, W. E. L., & Kikinis, R. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45786-0_48 · Cited by 98 · see citing works
Computer Vision for Biomedical Image Applications
Liu, Y., Jiang, T., & Zhang, C. (2005) · Lecture notes in computer science · doi:10.1007/11569541 · Cited by 97 · see citing works
Automated Segmentation of MRI of Brain Tumors
Kaus, M. R., Warfield, S. K., Nabavi, A., et al. (2001) · Cited by 87 · see citing works
Capelle, A.-S., Colot, O., & Fernández‐Maloigne, C. (2003) · Information Fusion · doi:10.1016/j.inffus.2003.10.001 · Cited by 84 · see citing works
Multiscale image segmentation using a hierarchical self-organizing map
Bhandarkar, S. M., Koh, J., & Suk, M. (1997) · Neurocomputing · doi:10.1016/s0925-2312(96)00048-3 · Cited by 82 · see citing works
Automated brain tumor segmentation using spatial accuracy-weighted hidden Markov Random Field
Nie, J., Xue, Z., Liu, T., et al. (2009) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2009.04.006 · Cited by 81 · see citing works
Chinnadurai, V., Damayanti, G., Pant, R., et al. (2007) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2007.04.004 · Cited by 79 · see citing works
Murugavall, S., & Rajamani, V. (2007) · Journal of Computer Science · doi:10.3844/jcssp.2007.841.846 · Cited by 78 · see citing works
A new segmentation system for brain MR images based on fuzzy techniques
Kannan, S. R. (2008) · Applied Soft Computing · doi:10.1016/j.asoc.2007.10.025 · Cited by 74 · see citing works
Deng, W., Xiao, W., Deng, H., et al. (2010) · International Conference on Biomedical Engineering and Informatics (BMEI) · doi:10.1109/bmei.2010.5639536 · Cited by 73 · see citing works
Case study: an evaluation of user-assisted hierarchical watershed segmentation
Cates, J. E., Whitaker, R., & Jones, G. (2005) · Medical Image Analysis · doi:10.1016/j.media.2005.04.007 · Cited by 71 · see citing works
Medical Image Segmentation: Methods and Applications in Functional Imaging
Wong, K. P. (2007) · Springer eBooks · doi:10.1007/0-306-48606-7_3 · Cited by 69 · see citing works
Skull stripping and automatic segmentation of brain MRI using seed growth and threshold techniques
Shanthi, K., & Kumar, M. S. (2007) · International Conference on Intelligent and Advanced Systems · doi:10.1109/icias.2007.4658421 · Cited by 65 · see citing works
WHITE, D. R. R., HOUSTON, A. S., Sampson, W., et al. (1999) · Clinical Nuclear Medicine · doi:10.1097/00003072-199903000-00008 · Cited by 54 · see citing works
A gradient magnitude based region growing algorithm for accurate segmentation
Sato, M., Lakare, S., Wan, M., et al. (2002) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2000.899432 · Cited by 49 · see citing works
Modified technique for volumetric brain tumor measurements
Salman, Y. M. (2009) · Journal of Biomedical Science and Engineering · doi:10.4236/jbise.2009.21003 · Cited by 40 · see citing works
A New Deformable Model Using Dynamic Gradient Vector Flow and Adaptive Balloon Forces
Luo, S., Li, R., & Sebastien Ourselin, A. (2003) · Cited by 40 · see citing works
Unsupervised segmentation for automatic detection of brain tumors in MRI
Capelle, A.-S., Alata, O., Fernandez, C., et al. (2002) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2000.901033 · Cited by 36 · see citing works
An electrostatic deformable model for medical image segmentation
Chang, H.-H., & Valentino, D. J. (2007) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2007.08.012 · Cited by 35 · see citing works
Tongue carcinoma: tumor volume measurement
Chong, V. F. H., Zhou, J., Khoo, J. B. K., et al. (2004) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2003.09.089 · Cited by 35 · see citing works
A Novel Approach for Segmentation of MRI Brain Images
Kong, J., Wang, J., Lu, Y., et al. (2006) · IEEE Mediterranean Electrotechnical Conference (MELECON) · doi:10.1109/melcon.2006.1653154 · Cited by 33 · see citing works
Automatic Brain Tumor Segmentation
Schmidt, M. (2005) · doi:10.7939/r3q52fd8g · Cited by 32 · see citing works
Using Neural Networks to Automatically Detect Brain Tumours in MR Images
Dickson, S., Thomas, B. T., & Goddard, P. (1997) · International Journal of Neural Systems · doi:10.1142/s0129065797000124 · Cited by 30 · see citing works
Bauer, S., Nolte, L. P., & Reyes, M. (2011) · IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI) · doi:10.1109/isbi.2011.5872808 · Cited by 29 · see citing works
Multispectral brain tumor segmentation based on histogram model adaptation
Rexilius, J., Hahn, H. K., Klein, J., et al. (2007) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.709410 · Cited by 28 · see citing works
Validation Techniques for Quantitative Brain Tumors Measurements
Salman, Y. M., Assal, M. A., Badawi, A., et al. (2005) · doi:10.1109/iembs.2005.1616129 · Cited by 28 · see citing works
New Techniques in Oncologic Imaging
(2005) · CRC Press eBooks · doi:10.1201/b21633 · Cited by 26 · see citing works
A Modified Fuzzy C-Means Algorithm for MR Brain Image Segmentation
Szilágyi, L. S., Szilágyi, S. M., & Benyó, Z. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74260-9_77 · Cited by 25 · see citing works
Statistical solution to watershed over-segmentation
Giès, V., & Bernard, T. M. (2005) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2004.1421440 · Cited by 25 · see citing works
3D variational brain tumor segmentation on a clustered feature set
Popuri, K., Cobzaş, D., Jägersand, M., et al. (2009) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.811029 · Cited by 24 · see citing works
A new fuzzy approach to brain tumor segmentation
Gordillo, N., Montseny, E., & Sobrevilla, P. (2010) · doi:10.1109/fuzzy.2010.5584178 · Cited by 22 · see citing works
Self-citationMultiparameter segmentation and quantization of brain tumor from MRI images.
Ratan, R. (2009) · Indian Journal of Science and Technology · doi:10.17485/ijst/2009/v2i2.4 · Cited by 22 · see citing works
Image Processing in Tumor Imaging
Yao, J. (2005) · CRC Press eBooks · doi:10.1201/b21633-8 · Cited by 21 · see citing works
Development of image-processing software for automatic segmentation of brain tumors in MR images
Chinnadurai, V., & Gharpure, D. (2011) · Journal of Medical Physics · doi:10.4103/0971-6203.83481 · Cited by 20 · see citing works
Automatic Brain Tumor Detection and Isolation of Tumor Cells from MRI Images
KumarKole, D., & Halder, A. (2012) · International Journal of Computer Applications · doi:10.5120/4905-7416 · Cited by 19 · see citing works
Automated Medical Image Segmentation Using a New Deformable Surface Model
Luo, S. (2006) · NOVA (University of Newcastle Australia) · Cited by 18 · see citing works
A fast deformable region model for brain tumor boundary extraction
Law, A., Lam, F. K., & Chan, F. H. Y. (2002) · doi:10.1109/iembs.2002.1106273 · Cited by 17 · see citing works
Segmentation of Magnetic Resonance Images Using Discrete Curve Evolution and Fuzzy Clustering
Supot, S., Chaichana, T., Chuchart, P., et al. (2007) · IEEE International Conference on Integration Technology · doi:10.1109/icitechnology.2007.4290409 · Cited by 17 · see citing works
Multiparameter Segmentation and Quantization of Brain Tumor from MRI Images
Ratan, R., Sharma, S., & Sharma, S. (2009) · Indian Journal of Science and Technology · doi:10.17485/ijst/2009/v2i2/29385 · Cited by 16 · see citing works
Integrating automatic and interactive brain tumor segmentation
Dam, E. B., Loog, M., & Letteboer, M. M. J. (2004) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2004.1334647 · Cited by 14 · see citing works
Unsupervised Segmentation Using Fuzzy Logicbased Texture Spectrum For Mri Brain Images
Jiji, G. W., & Ganesan, L. (2007) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1061448 · Cited by 13 · see citing works
Advanced Segmentation Techniques
Farag, A. A., Ahmed, M. N., El-Baz, A. S., et al. (2005) · Springer eBooks · doi:10.1007/0-306-48551-6_9 · Cited by 12 · see citing works
Automatic MRI Brain Tumor Segmentation System Based on Localizing Active Contour Models
Wang, Y., Lin, Z., Cao, J., et al. (2011) · Advanced materials research · doi:10.4028/www.scientific.net/amr.219-220.1342 · Cited by 11 · see citing works
Fuzzy Spatial Growing for Glioblastoma Multiforme Segmentation on Brain Magnetic Resonance Imaging
Veloz, A., Chabert, S., Rodrigo Salas, F., et al. (2007) · Springer eBooks · doi:10.1007/978-3-540-76725-1_89 · Cited by 9 · see citing works
Image segmentation using a charged fluid method
Chang, H.-H., & Valentino, D. J. (2006) · Journal of Electronic Imaging · doi:10.1117/1.2199555 · Cited by 9 · see citing works
Tayel, M. B., & Abdou, M. A. (2006) · doi:10.1109/nrsc.2006.386387 · Cited by 6 · see citing works
Sing, J. K., Khan, K., Basu, D., et al. (2011) · International Conference on Communications and Signal Processing (ICCSP) · doi:10.1109/iccsp.2011.5739347 · Cited by 5 · see citing works
Kazerooni, A. F., Ahmadian, A., Saberi, H., et al. (2012) · International Conference on Information Science, Signal Processing and their Applications (ISSPA) · doi:10.1109/isspa.2012.6310555 · Cited by 4 · see citing works
A Fully Automatic Approach to Detect Brain Cancer Using Random Walk Algorithm
Choubey, M., & Agrawal, S. (2015) · Cited by 4 · see citing works
Design of Embedded Monitored Control System for Ultrasonic Reaction Kettle
Wang, B. Q., & Yang, L. (2011) · Advanced materials research · doi:10.4028/www.scientific.net/amr.339.219 · Cited by 1 · see citing works
Methods and Applications for Segmenting 3D medical Image Data
Shen, H. (2006) · IGI Global eBooks · doi:10.4018/978-1-59140-753-9.ch012 · Cited by 1 · see citing works
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Cheng, J., Huang, W., Cao, S., Yang, R., Yang, W., Yun, Z., Wang, Z., & Feng, Q. (2015) · PLOS ONE · doi:10.1371/journal.pone.0140381 · Cited by 879 · see citing works
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Işın, A., Direkoğlu, C., & Şah, M. (2016) · Procedia Computer Science · doi:10.1016/j.procs.2016.09.407 · Cited by 766 · see citing works
Abd-Ellah, M. K., Awad, A. I., Khalaf, A. A. M., & Hamed, H. F. A. (2019) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2019.05.028 · Cited by 348 · see citing works
Amin, J., Sharif, M., Yasmin, M., Saba, T., Anjum, M. A., & Fernandes, S. L. (2019) · Journal of Medical Systems · doi:10.1007/s10916-019-1453-8 · Cited by 147 · see citing works
Naeem, A., Anees, T., Naqvi, R. A., & Loh, W.-K. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12020275 · Cited by 103 · see citing works
- Authors
- Gordillo, N., Montseny, E., & Sobrevilla, P.
- Affiliations
- Universidad Autónoma de Ciudad Juárez (MX)
- Universitat Politècnica de Catalunya (ES)
- Corresponding author
- Gordillo, Nelly
- ORCID
- Gordillo, Nelly
- Venue
- Magnetic Resonance Imaging
- Publisher
- Elsevier BV
- Published
- 2013-10
- Volume
- 31
- Issue
- 8
- Pages
- 1426-1438
- ISSN
- 0730-725X (ISSN-L)
- OpenAlex
- W2063552084
- Cited by
- 697
- References
- 100
- Citations by year
- 2026: 20 · 2025: 45 · 2024: 44 · 2023: 72 · 2022: 79 · 2021: 66 · 2020: 68 · 2019: 74 · 2018: 66 · 2017: 64 · 2016: 46 · 2015: 36 · 2014: 16 · 2013: 1
- FWCI
- 3.18
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- public-domain
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Locations
- Magnetic Resonance Imaging (publishedVersion)
- PubMed (publishedVersion)
- UPCommons institutional repository (Universitat Politècnica de Catalunya) (publishedVersion)
- RECERCAT (Consorci de Serveis Universitaris de Catalunya) (submittedVersion, public-domain)
- Topics
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Genetics › Glioma Diagnosis and Treatment
- Keywords
- brain tumor segmentation, magnetic resonance imaging, tumor edema, tumor necrosis, gray matter, white matter, cerebrospinal fluid, semi-automatic segmentation, automatic segmentation, medical image segmentation
- MeSH terms
- Algorithms, Animals, Artificial Intelligence, Brain, Brain Neoplasms, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Models, Biological, Pattern Recognition, Automated, Sensitivity and Specificity, Reproducibility of Results, Models, Statistical, Imaging, Three-Dimensional
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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