Education, Science, Technology, Innovation and Life
Open Access
Sign In
  1. Home
  2. Journals
  3. Advances in Computer, Signals and Systems
  4. Vol 7, Issue 8, 2023

Review of deep learning-driven MRI brain tumor detection and segmentation methods

Download as PDF

DOI: 10.23977/acss.2023.070803 | Downloads: 36 | Views: 504

Author(s)

Rong Zhang 1, Hongliang Luo 1, Weijie Chen 1, Yongqiang Bai 1

Affiliation(s)

1 Zhejiang Wanli University, Ningbo, Zhejiang, 315000, China

Corresponding Author

Rong Zhang

ABSTRACT

The application of deep learning in the field of medical imaging has become increasingly widespread, greatly promoting the advancement and development of Magnetic Resonance Imaging (MRI) brain tumor detection and segmentation techniques. Therefore, a comprehensive review of deep learning-based methods for MRI brain tumor detection and segmentation was conducted. This review introduces the basic concepts of brain tumors and MRI brain tumor detection and segmentation, discusses the specific applications and typical methods of deep learning in MRI brain tumor detection and segmentation, and analyzes and compares the performance and advantages and disadvantages of different methods. Additionally, representative brain tu-mor segmentation dataset (BraTS) and its evaluation metrics are introduced, upon which the performance of various deep learning-based brain tumor segmentation methods on the BraTS 2019-2022 dataset is compared. Lastly, the challenges and future development trends in deep learning-based MRI brain tumor detection and segmentation methods are summarized and anticipated.

KEYWORDS

Deep Learning, Brain Tumor, Detection and Segmentation, Magnetic Resonance Imaging

CITE THIS PAPER

Rong Zhang, Hongliang Luo, Weijie Chen, Yongqiang Bai, Review of deep learning-driven MRI brain tumor detection and segmentation methods. Advances in Computer, Signals and Systems (2023) Vol. 7: 17-28. DOI: http://dx.doi.org/10.23977/acss.2023.070803.

REFERENCES

[1] Solanki, S., Singh, U.P., Chouhan, S.S. and Jain, S. Brain Tumor Detection and Classification using Intelligence Techniques: An Overview. IEEE Access, (2023).
[2] Jo, S., Sun, I.C., Ahn, C.H., Lee, S. and Kim, K. Recent trend of ultrasound-mediated nanoparticle delivery for brain imaging and treatment. ACS applied materials & interfaces, (2022), 15(1), 120-137.
[3] Scola, E., Desideri, I., Bianchi, A., Gadda, D., Busto, G., Fiorenza, A. and Fainardi, E. Assessment of brain tumors by magnetic resonance dynamic susceptibility contrast perfusion-weighted imaging and computed tomography perfusion: A comparison study. La radiologia medica, (2022), 127(6), 664-672.
[4] Mehnatkesh, H., Jalali, S.M.J., Khosravi, A. and Nahavandi, S. An intelligent driven deep residual learning framework for brain tumor classification using MRI images. Expert Systems with Applications, (2023), 213, 119087.
[5] Reddy, K.R. and Dhuli, R. Detection of brain tumors from MR images using fuzzy thresholding and texture feature descriptor. The Journal of Supercomputing, (2023), 79(8), 9288-9319.
[6] Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H. and Xu, D. Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images. arXiv preprint arXiv:2201.01266, (2022).
[7] Abdel-Gawad, A.H., Said, L.A. and Radwan, A.G. Optimized edge detection technique for brain tumor detection in MR images. IEEE Access, (2020), 8, 136243-136259.
[8] Rehman, A., Khan, M.A., Saba, T., Mehmood, Z., Tariq, U. and Ayesha, N. Microscopic brain tumor detection and classification using 3D CNN and feature selection architecture. Microscopy Research and Technique, (2021), 84(1), 133-149.
[9] Jun, W. and Liyuan, Z. Brain Tumor Classification Based on Attention Guided Deep Learning Model. International Journal of Computational Intelligence Systems, (2022), 15(1), 35.
[10] Khan, M.A., Khan, A., Alhaisoni, M., Alqahtani, A., Alsubai, S., Alharbi, M. and Damaševičius, R. Multimodal brain tumor detection and classification using deep saliency map and improved dragonfly optimization algorithm. International Journal of Imaging Systems and Technology, (2023), 33(2), 572-587.
[11] Anithadevi, D. and Perumal, K. A hybrid approach based segmentation technique for brain tumor in MRI Images. arXiv preprint arXiv:1603.02447, (2016).
[12] Csaholczi, S., Iclănzan, D., Kovács, L. and Szilágyi, L. Brain tumor segmentation from multi-spectral MR image data using random forest classifier. In International Conference on Neural Information Processing (pp. 174-184). Cham: Springer International Publishing, (2020).
[13] Khan, A., Akbar, S., Alghanim, A., Hassan, S.A. and Ayesha, N. Automated Hybrid Model for Detection and Classification of Brain Tumor. In 2023 Sixth International Conference of Women in Data Science at Prince Sultan University (WiDS PSU) (pp. 79-84). IEEE, (2023).
[14] Bindu, N.P. and Sastry, P.N. Automated brain tumor detection and segmentation using modified UNet and ResNet model. Soft Computing, (2023), 27(13), 9179-9189.
[15] Balamurugan, T. and Gnanamanoharan, E. Brain tumor segmentation and classification using hybrid deep CNN with LuNetClassifier. Neural Computing and Applications, (2023), 35(6), 4739-4753.
[16] Saeidifar, M., Yazdi, M. and Zolghadrasli, A. Performance improvement in brain tumor detection in MRI images using a combination of evolutionary algorithms and active contour method. Journal of Digital Imaging, (2021), 34, 1209-1224.
[17] Rao, C.S. and Karunakara, K. Efficient detection and classification of brain tumor using kernel based SVM for MRI. Multimedia Tools and Applications, (2022), 81(5), 7393-7417.
[18] Siddique, M.A.B., Sakib, S., Khan, M.M.R., Tanzeem, A.K., Chowdhury, M. and Yasmin, N. Deep convolutional neural networks model-based brain tumor detection in brain MRI images. In 2020 Fourth International Conference on I-SMAC. IEEE, (2020).
[19] Saeedi, S., Rezayi, S., Keshavarz, H. and R Niakan Kalhori, S. MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques. BMC Medical Informatics and Decision Making, (2023), 23(1), 1-17.
[20] Ren, S., He, K., Girshick, R. and Sun, J. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, (2015), 28.
[21] Yilmaz, A. Brain tumor detection from MRI images with using proposed deep learningmodel: the partial correlation-based channel selection. Turkish Journal of Electrical Engineering and Computer Sciences, (2021), 29(8), 2615-2633.
[22] Chattopadhyay, A. and Maitra, M. MRI-based brain tumour image detection using CNN based deep learning method. Neuroscience informatics, (2022), 2(4), 100060.
[23] Sarala, B., Sumathy, G., Kalpana, A.V. and Hephzipah, J.J. Glioma brain tumor detection using dual convolutional neural networks and histogram density segmentation algorithm. Biomedical Signal Processing and Control, (2023), 85, 104859.
[24] Babayomi, M., Olagbaju, O.A. and Kadiri, A.A. Convolutional XGBoost (C-XGBOOST) Model for Brain Tumor Detection. arXiv preprint arXiv:2301.02317, (2023).
[25] Mostafiz, R., Uddin, M.S., Alam, N.A., Hasan, M.M. and Rahman, M.M. MRI-based brain tumor detection using the fusion of histogram oriented gradients and neural features. Evolutionary Intelligence, (2021), 14, 1075-1087.
[26] Rai, H.M. and Chatterjee, K. 2D MRI image analysis and brain tumor detection using deep learning CNN model LeU-Net. Multimedia Tools and Applications, (2021), 80, 36111-36141.
[27] Jabbar, A., Naseem, S., Mahmood, T., Saba, T., Alamri, F.S. and Rehman, A. Brain Tumor Detection and Multi-Grade Segmentation through Hybrid Caps-VGGNet Model. IEEE Access, (2023).
[28] Maurya, U., Kalyan, A.K., Bohidar, S. and Sivakumar, D.S. Detection and Classification of Glioblastoma Brain Tumor. arXiv preprint arXiv:2304.09133, (2023).
[29] LeCun, Y., Bottou, L., Bengio, Y. and Haffner, P. Gradient-based learning applied to document recognition. Proceedings of the IEEE, (1998), 86(11), 2278-2324.
[30] Krizhevsky, A., Sutskever, I. and Hinton, G.E. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, (2012), 25.
[31] Anand, V.K., Grampurohit, S., Aurangabadkar, P., Kori, A., Khened, M., Bhat, R.S. and Krishnamurthi, G. Brain tumor segmentation and survival prediction using automatic hard mining in 3D CNN architecture. 6th International Workshop, BrainLes, 2020, Springer International Pub-lishing, (2021): 310-319.
[32] Li, X., Chen, H., Qi, X., Dou, Q., Fu, C.W. and Heng, P.A. H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes. IEEE transactions on medical imaging, (2018), 37(12), 2663-2674.
[33] Babu, P.A., Rao, B.S., Reddy, Y.V. B., Kumar, G.R., Rao, J.N., Koduru, S.K.R. and Kumar, G.S. Optimized CNN-based Brain Tumor Segmentation and Classification using Artificial Bee Colony and Thresholding. International Journal of Computers Communications & Control, (2023), 18(1).
[34] Long, J., Shelhamer, E. and Darrell, T. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition. (2015).
[35] Shen, H., Wang, R., Zhang, J. and McKenna, S.J. Boundary-aware fully convolutional network for brain tumor segmentation. In Lecture Notes in Computer Science Medical Image Computing and Computer-Assisted Intervention. MICCAI, (2017).
[36] Sun, J., Peng, Y., Guo, Y. and Li, D. Segmentation of the multimodal brain tumor image used the multi-pathway architecture method based on 3D FCN. Neurocomputing, (2021), 423, 34–45.
[37] Zhou, Z., He, Z. and Jia, Y. AFPNet: A 3D fully convolutional neural network with atrous-convolution feature pyramid for brain tumor segmentation via MRI images. Neurocomputing, (2020). 235–244.
[38] Mzoughi, H., Njeh, I., Slima, M.B. and Benhamida, A. Glioblastomas brain Tumor Segmentation using Optimized U-Net based on Deep Fully Convolutional Networks (D-FCNs). 2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), (2020).
[39] Xiao, X., Lian, S., Luo, Z. and Li, S. Weighted Res-UNet for High-Quality Retina Vessel Segmentation. 2018 9th International Conference on Information Technology in Medicine and Education (ITME), (2018).
[40] Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N. and Liang, J. Unet++: A nested u-net architecture for medical image segmentation. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support Lecture Notes in Computer Science (pp. 3–11), (2018).
[41] Oktay, O., Schlemper, J., Folgoc, L., Lee, Matthew C. H., Heinrich, Mattias P., Misawa, K. and Rueckert, D. Attention U-Net: Learning Where to Look for the Pancreas. arXiv: Computer Vision and Pattern Recognition, (2018).
[42] Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T. and Ronneberger, O. 3D U-Net: learning dense volumetric segmentation from sparse annotation. Springer International Publishing, (2016). 
[43] Milletari, F., Navab, N. and Ahmadi, S.A. V-net: Fully convolutional neural networks for volumetric medical image segmentation. In 2016 fourth international conference on 3D vision (3DV) (pp. 565-571). IEEE, (2016).
[44] Badrinarayanan, V., Kendall, A. and Cipolla, R. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence, (2017), 39(12), 2481-2495. 
[45] Zhang, L., Lan, C., Fu, L., Mao, X. and Zhang, M. Segmentation of brain tumor MRI image based on improved attention module Unet network. Signal, Image and Video Processing, (2023), 17(5), 2277-2285. 
[46] Nodirov, J., Abdusalomov, A.B. and Whangbo, T.K. Attention 3D U-Net with Multiple Skip Connections for Segmentation of Brain Tumor Images. Sensors, (2022), 22(17), 6501. 
[47] Anaya-Isaza, A., Mera-Jiménez, L. and Fernandez-Quilez, A. CrossTransUnet: A new computationally inexpensive tumor segmentation model for brain MRI. IEEE Access, (2023), 11, 27066-27085. 
[48] Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J. and Van Leemput, K. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE transactions on medical imaging, (2014), 34(10), 1993-2024. 
[49] Erickson, B., Akkus, Z., Sedlar, J. and Kofiatis, P. Data from LGG-1p19qDeletion. The Cancer Imaging Archive, (2017), 76(10.7937), K9. 
[50] Maier, O., Menze, B.H., Von der Gablentz, J., Häni, L., Heinrich, M.P., Liebrand, M. and Reyes, M. ISLES 2015-A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI. Medical image analysis, (2017), 35, 250-269.
[51] Greenspan, H., Van Ginneken, B. and Summers, R.M. Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique. IEEE transactions on medical imaging, (2016), 35(5), 1153-1159.
[52] Decuyper, M., Bonte, S., Deblaere, K. and Van Holen, R. Automated MRI based pipeline for segmentation and prediction of grade, IDH mutation and 1p19q co-deletion in glioma. Computerized Medical Imaging and Graphics, (2021), 88, 101831.
[53] Karri, M., Annvarapu, C.S.R. and Acharya, U.R. SGC-ARANet: scale-wise global contextual axile reverse attention network for automatic brain tumor segmentation. Applied Intelligence, (2023), 53(12), 15407-15423.
[54] Medeiros, T.A., Saraiva Junior, R.G., Nascimento, F.A.D.O. and Carvalho, J.L.A.D. Classification of 1p/19q Status in Low-Grade Gliomas: Experiments with Radiomic Features and Ensemble-Based Machine Learning Methods. Brazilian Archives of Biology and Technology, (2023), 66, e23230002.
[55] Ahmad, P., Jin, H., Alroobaea, R., Qamar, S., Zheng, R., Alnajjar, F. and Aboudi, F. MH UNet: A multi-scale hierarchical based architecture for medical image segmentation. IEEE Access, (2021), 9, 148384-148408.
[56] Zhang, Y., Liu, S., Li, C. and Wang, J. Application of deep learning method on ischemic stroke lesion segmentation. Journal of Shanghai Jiaotong University (Science), (2022), 1-13.
[57] Li, L., Ma, K., Song, Y. and Du, X. TSRL-Net: Target-aware supervision residual learning for stroke segmentation. Computers in Biology and Medicine, (2023), 159, 106840.
[58] Zeineldin, R.A., Karar, M.E., Burgert, O. and Mathis-Ullrich, F. Multimodal CNN networks for brain tumor segmentation in MRI: a BraTS 2022 challenge solution. arXiv preprint arXiv:2212.09310, (2022).
[59] Messaoudi, H., Belaid, A., Salem, D.B. and Conze, P.H. Cross-dimensional transfer learning in medical image segmentation with deep learning. Medical Image Analysis, (2023), 102868.
[60] Ma, J. and Chen, J. NnUNet with Region-based Training and Loss Ensembles for Brain Tumor Segmentation. In International MICCAI Brainlesion Workshop (pp. 421-430). Cham: Springer International Publishing, (2021).
[61] Wu, H.Y. and Lin, Y.L. HarDNet-BTS: A Harmonic Shortcut Network for Brain Tumor Segmentation. In International MICCAI Brainlesion Workshop (pp. 261-271). Cham: Springer International Publishing, (2021).
[62] Milesi, A., Futrega, M., Marcinkiewicz, M. and Ribalta, P. Brain tumor segmentation using neural network topology search. In International MICCAI Brainlesion Workshop (pp. 366-376). Cham: Springer International Publishing, (2021).
[63] Fidon, L., Ourselin, S. and Vercauteren, T. Generalized wasserstein dice score, distributionally robust deep learning, and ranger for brain tumor segmentation: BraTS 2020 challenge. Springer International Publishing, (2021).
[64] Henry, T., Carré, A., Lerousseau, M., Estienne, T., Robert, C., Paragios, N. and Deutsch, E. Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solution. Springer International Publishing, (2021).
[65] Jun, W., Haoxiang, X. and Wang, Z. Brain tumor segmentation using dual-path attention U-net in 3D MRI images. Springer International Publishing, (2021).
[66] Liu, H., Huo, G., Li, Q., Guan, X. and Tseng, M.L. Multiscale lightweight 3D segmentation algorithm with attention mechanism: Brain tumor image segmentation. Expert Systems with Applications, (2023), 214, 119166.
[67] Zhou, T. and Zhu, S. Uncertainty quantification and attention-aware fusion guided multi-modal MR brain tumor segmentation. Computers in Biology and Medicine, (2023), 107142.

Subscription
E-Mail Alert
Most popular papers
Call for Special Issue
Publication Ethics & OA Statement
Plagiarism Policy
Contact Us
Downloads: 16035
Visits: 271269

Sponsors, Associates, and Links

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.