Review of MRI-based brain tumor image segmentation using deep learning methods
Open accessAbstract
Brain tumor segmentation is an important task in medical image processing. Early diagnosis of brain tumors plays an important role in improving treatment possibilities and increases the survival rate of the patients. Manual segmentation of the brain tumors for cancer diagnosis, from large amount of MRI images generated in clinical routine, is a difficult and time consuming task. There is a need for automatic brain tumor image segmentation. The purpose of this paper is to provide a review of MRI-based brain tumor segmentation methods. Recently, automatic segmentation using deep learning methods proved popular since these methods achieve the state-of-the-art results and can address this problem better than other methods. Deep learning methods can also enable efficient processing and objective evaluation of the large amounts of MRI-based image data. There are number of existing review papers, focusing on traditional methods for MRI-based brain tumor image segmentation. Different than others, in this paper, we focus on the recent trend of deep learning methods in this field. First, an introduction to brain tumors and methods for brain tumor segmentation is given. Then, the state-of-the-art algorithms with a focus on recent trend of deep learning methods are discussed. Finally, an assessment of the current state is presented and future developments to standardize MRI-based brain tumor segmentation methods into daily clinical routine are addressed.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
The multimodal brain tumor image segmentation benchmark (BRATS)
Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2377694 · Cited by 6,781 · see citing works
Brain tumor segmentation with Deep Neural Networks
Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.-M., & Larochelle, H. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.05.004 · Cited by 3,278 · see citing works
Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images
Pereira, S., Pinto, A., Alves, V., & Silva, C. A. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2538465 · Cited by 2,735 · see citing works
DeAngelis, L. M. (2001) · New England Journal of Medicine · doi:10.1056/nejm200101113440207 · Cited by 1,689 · see citing works
Representing and Recognizing the Visual Appearance of Materials Using Three-Dimensional Textons
Leung, T., & Malik, J. (2001) · International Journal of Computer Vision · doi:10.1023/a:1011126920638 · Cited by 1,498 · cited 1× · see citing works
“Features like, asymmetry-related features 19 , discrete wavelet transforms 20 (DWT), textons 21 , multifractal Brownian motion features 22 , first order statistical features 19 , raw intensities, local image textures, intensity gradients and edge based features 12 are some examples.”
Background(2012) · neural information processing systems · Cited by 1,291 · see citing works
A survey of MRI-based medical image analysis for brain tumor studies
Bauer, S., Wiest, R., Nolte, L.-P., & Reyes, M. (2013) · Physics in Medicine and Biology · doi:10.1088/0031-9155/58/13/r97 · Cited by 1,036 · see citing works
State of the art survey on MRI brain tumor segmentation
Gordillo, N., Montseny, E., & Sobrevilla, P. (2013) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2013.05.002 · Cited by 697 · see citing works
Wavelet-based Rician noise removal for magnetic resonance imaging
Nowak, R. D. (1999) · IEEE Transactions on Image Processing · doi:10.1109/83.791966 · Cited by 613 · see citing works
A survey of MRI-based brain tumor segmentation methods
Liu, J., Li, M., Wang, J., Wu, F., Liu, T., & Pan, Y. F. (2014) · Tsinghua Science & Technology · doi:10.1109/tst.2014.6961028 · Cited by 399 · see citing works
Bauer, S., Nolte, L. P., & Reyes, M. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23626-6_44 · Cited by 381 · see citing works
Multifractal Texture Estimation for Detection and Segmentation of Brain Tumors
Islam, A., Reza, S. M. S., & Iftekharuddin, K. M. (2013) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2271383 · Cited by 319 · see citing works
Tumor-Cut: Segmentation of Brain Tumors on Contrast Enhanced MR Images for Radiosurgery Applications
Hamamci, A., Kucuk, N., Karaman, K., Engin, K., & Unal, G. (2012) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2011.2181857 · Cited by 286 · see citing works
Tustison, N. J., Shrinidhi, K. L., Wintermark, M., Durst, C. R., Kandel, B. M., Gee, J. C., Grossman, M., & Avants, B. (2014) · Neuroinformatics · doi:10.1007/s12021-014-9245-2 · Cited by 277 · see citing works
Decision Forests for Tissue-Specific Segmentation of High-Grade Gliomas in Multi-channel MR
Zikic, D., Glocker, B., Konukoglu, E., Criminisi, A., Demiralp, C., Shotton, J., Thomas, O. M., Das, T., Jena, R., & Price, S. J. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33454-2_46 · Cited by 276 · see citing works
(2014) · Cited by 234 · see citing works
Brain tumour classification using two‐tier classifier with adaptive segmentation technique
Anitha, V., & Murugavalli, S. (2015) · IET Computer Vision · doi:10.1049/iet-cvi.2014.0193 · Cited by 220 · see citing works
(2014) · Cited by 188 · see citing works
Evaluating intensity normalization on MRIs of human brain with multiple sclerosis
Shah, M., Xiao, Y., Subbanna, N. K., Francis, S., Arnold, D. L., Collins, D. L., & Arbel, T. (2010) · Medical Image Analysis · doi:10.1016/j.media.2010.12.003 · Cited by 179 · 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
Simulation of brain tumors in MR images for evaluation of segmentation efficacy
Prastawa, M. W., Bullitt, E., & Gerig, G. (2008) · Medical Image Analysis · doi:10.1016/j.media.2008.11.002 · Cited by 145 · see citing works
Malignant glioma: ESMO Clinical Recommendations for diagnosis, treatment and follow-up
Stupp, R., & Roila, F. (2009) · Annals of Oncology · doi:10.1093/annonc/mdp151 · Cited by 142 · see citing works
Angelini, E. D., Clatz, O., Mandonnet, E., Konukoğlu, E., Capelle, L., & Duffau, H. (2007) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/157340507782446241 · Cited by 121 · see citing works
Combining Generative Models for Multifocal Glioma Segmentation and Registration
Kwon, D., Shinohara, R. T., Akbari, H., & Davatzikos, C. A. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10404-1_95 · Cited by 100 · see citing works
Within-brain classification for brain tumor segmentation
Havaei, M., Larochelle, H., Poulin, P., et al. (2015) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-015-1311-1 · Cited by 79 · 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
Hereditary Tumor Syndromes and Gliomas
Reuß, D., & von Deimling, A. (2009) · Recent results in cancer research/Recent Results in Cancer Research · doi:10.1007/978-3-540-31206-2_5 · Cited by 52 · see citing works
Malignant glioma: ESMO Clinical Recommendations for diagnosis, treatment and follow-up
Stupp, R. (2007) · Annals of Oncology · doi:10.1093/annonc/mdm044 · Cited by 22 · see citing works
Flagged first, then by citations
Research works printed but not linked: 5
- Deimling A. Gliomas. Recent Results in Cancer Research vol 171. Berlin: Springer; 2009.
- Drevelegas A and Papanikolou N. Imaging modalities in brain tumors Imaging of Brain Tumors with Histological Correlations. Berlin: Springer; 2011; chapter 2:13-34.
- Davy A. et al. Brain tumor segmentation with deep neural networks. MICCAI Multimodal Brain Tumor Segmentation Challenge (BraTS) 2014:1-5.
- Dvorak P, Menze B. Structured prediction with convolutional neural networks for multimodal brain tumor segmentation. MICCAI Multimodal Brain Tumor Segmentation Challenge (BraTS) 2015:13-24.
- Rao V, Sarabi M S, Jaiswal A. Brain tumor segmentation with deep learning. MICCAI Multimodal Brain Tumor Segmentation Challenge (BraTS) 2015:56-59.
Cited by
Sorted by citations
Gumaei, A., Hassan, M. M., Hassan, M. R., Alelaiwi, A., & Fortino, G. (2019) · IEEE Access · doi:10.1109/access.2019.2904145 · Cited by 484 · see citing works
Explainable, trustworthy, and ethical machine learning for healthcare: A survey
Rasheed, K., Qayyum, A., Ghaly, M., Al-Fuqaha, A., Razi, A., & Qadir, J. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106043 · Cited by 372 · 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
Data Augmentation for Brain-Tumor Segmentation: A Review
Nalepa, J., Marcinkiewicz, M., & Kawulok, M. (2019) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2019.00083 · Cited by 340 · see citing works
Survey on deep learning for radiotherapy
Meyer, P., Noblet, V., Mazzara, C., & Lallement, A. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.05.018 · Cited by 302 · see citing works
Semantic segmentation using Vision Transformers: A survey
Thisanke, H., Deshan, C., Chamith, K., Seneviratne, S., Vidanaarachchi, R., & Herath, D. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106669 · Cited by 288 · see citing works
Sharif, M., Amin, J., Raza, M., Yasmin, M., & Satapathy, S. C. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.11.017 · Cited by 206 · see citing works
Hu, K., Gan, Q., Zhang, Y., Deng, S., Xiao, F., Huang, W., Cao, C., & Gao, X. (2019) · IEEE Access · doi:10.1109/access.2019.2927433 · Cited by 188 · see citing works
Abd-Ellah, M. K., Awad, A. I., Khalaf, A. A. M., & Hamed, H. F. A. (2018) · EURASIP Journal on Image and Video Processing · doi:10.1186/s13640-018-0332-4 · Cited by 170 · see citing works
Exploring deep learning for complex trait genomic prediction in polyploid outcrossing species
Zingaretti, L. M., Gezan, S. A., Ferrão, L. F. V., Osorio, L. F., Monfort, A., Muñoz, P. R., Whitaker, V. M., & Pérez-Enciso, M. (2020) · Frontiers in Plant Science · doi:10.3389/fpls.2020.00025 · Cited by 159 · see citing works
Artificial intelligence and its potential in oncology
Londhe, V. Y., & Bhasin, B. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2018.10.005 · Cited by 53 · see citing works
Grøvik, E., Yi, D., Iv, M., Tong, E., Nilsen, L. B., Latysheva, A., Saxhaug, C., Jacobsen, K. D., Helland, Å., Emblem, K. E., et al. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00398-4 · Cited by 51 · see citing works
- Authors
- Işın, A., Direkoğlu, C., & Şah, M.
- Affiliations
- Near East University (CY)
- Middle East Technical University (TR)
- Corresponding author
- Işın, Ali
- Venue
- Procedia Computer Science
- Publisher
- Elsevier BV
- Published
- 2016
- Volume
- 102
- Pages
- 317-324
- ISSN
- 1877-0509 (ISSN-L)
- OpenAlex
- W2538556778
- Cited by
- 766
- References
- 29
- Citations by year
- 2026: 37 · 2025: 58 · 2024: 114 · 2023: 116 · 2022: 131 · 2021: 112 · 2020: 88 · 2019: 69 · 2018: 32 · 2017: 8 · 2015: 1
- FWCI
- 40.33
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Procedia Computer Science (publishedVersion, cc-by-nc-nd)
- OpenMETU (Middle East Technical University) (submittedVersion, cc-by-nc-nd)
- 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, deep learning, automated image segmentation, medical image processing, brain tumors
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