Glioma Tumor Segmentation from Multi-Modal MRI Images Using Deep Residual and Inception U-Nets
Keywords:
Glioma, Brain Tumor, Deep Learning, Segmentation, U-Net, Inception, RresidualAbstract
A glioma is a primary brain tumor that develops within the brain's tissue. It originates from glial cells and keeps neuron in a specific place for normal brain function. Glioma affects all age people, mostly men than women. However, manually segmenting gliomas is a laborious and error-prone task. Recently, researchers working on imaging techniques along with recent deep neural networks (DNN) to segment tumor from the brain tissue. DNN generates features from images without feature engineering procedure. It is a popular technique, especially in medical imaging fields like precise segmentation, image classification and etc. By implementing early treatment strategies, it is possible to improve survival rates and reduce computational time. The main problems with automatic DNN models are bias toward a specific class, imbalanced data, local and global contexts, large training parameters, and accuracy. This research paper analyzes 2D multi-modal magnetic resonance imaging (MRI) image sequences to differentiate between tumor regions and normal brain regions. These models consist of two U-Net topologies for glioma tumor segmentation. The first U-Net incorporates an innovative inception structure design, which leads to exceptional segmentation performance for the whole tumor (WT). The inception structure gathers multi-scale feature maps from various kernels and concatenation layer. On the other hand, the second U-Net tackles degradation problems and reduces training errors, making optimization less complex compared to conventional DNNs. The two-path residual network captures crucial information. In terms of performance and accuracy, the proposed models surpass state-of-the-art models. The high-performance models give great contribution to researchers and physicians for computer vision applications.
Downloads
References
Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas. (2015). New England Journal of Medicine, 372(26), 2481–2498. https://doi.org/10.1056/nejmoa1402121.
Hanif, F., Muzaffar, K., Perveen, K., Malhi, S. M., & Simjee, S. U. (2017). Glioblastoma multiforme: A review of its epidemiology and pathogenesis through clinical presentation and treatment. Asian Pacific Journal of Cancer Prevention, 18(1), 3–9. https://doi.org/10.22034/APJCP.2017.18.1.3.
Dugdale, E., & Gerrard, G. (2006). Low-grade gliomas in adults. CME Cancer Medicine, 3(3), 40–45. https://doi.org/10.3171/2011.7.jns10238.
Venkataramanaiah, B., Sambath Kumar, K., & Giridhar Reddy, K. (2023). Brain Disorders Identification by Machine Learning Classifiers. Journal of Physics: Conference Series, 2466(1). https://doi.org/10.1088/1742-6596/2466/1/012036
Venkataramanaiah, B., & Kamala, J. (2020). ECG signal processing and KNN classifier-based abnormality detection by VH-doctor for remote cardiac healthcare monitoring. Soft Computing, 24(22), 17457–17466. https://doi.org/10.1007/s00500-020-05191-1.
Kumar, K. S., & Rajendran, A. (2021). Machine Learning Classifiers in Health Care. In Machine Learning and Analytics in Healthcare Systems. https://doi.org/10.1201/9781003185246-5
Pereira, S., Pinto, A., Alves, V., & Silva, C. A. (2016). Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images. IEEE Transactions on Medical Imaging, 35(5), 1240–1251. https://doi.org/10.1109/TMI.2016.2538465.
Navab, N., Hornegger, J., Wells, W. M., & Frangi, A. F. (2015). Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015: 18th International Conference Munich, Germany, October 5-9, 2015 proceedings, part III. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9351(Cvd), 12–20. https://doi.org/10.1007/978-3-319-24574-4.
Wang, S., Hu, S.-Y., Cheah, E., Wang, X., Wang, J., Chen, L., … Samir, A. (2020). U-Net Using Stacked Dilated Convolutions for Medical Image Segmentation. Retrieved from http://arxiv.org/abs/2004.03466.
Sarvamangala, D. R., & Kulkarni, R. V. (2021). Convolutional neural networks in medical image understanding: a survey. Evolutionary Intelligence, (0123456789). https://doi.org/10.1007/s12065-020-00540-3.
Diakogiannis, F. I., Waldner, F., Caccetta, P., & Wu, C. (2020). ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data. ISPRS Journal of Photogrammetry and Remote Sensing, 162, 94–114. https://doi.org/10.1016/j.isprsjprs.2020.01.013.
Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12), 2481–2495. https://doi.org/10.1109/TPAMI.2016.2644615.
Ding, Y., Li, C., Yang, Q., Qin, Z., & Qin, Z. (2019). How to Improve the Deep Residual Network to Segment Multi-Modal Brain Tumor Images. IEEE Access, 7, 152821–152831. https://doi.org/10.1109/ACCESS.2019.2948120.
Jha, D., Riegler, M. A., Johansen, D., Halvorsen, P., & Johansen, H. D. (2020). DoubleU-Net: A deep convolutional neural network for medical image segmentation. Proceedings - IEEE Symposium on Computer-Based Medical Systems, 2020-July(1), 558–564. https://doi.org/10.1109/CBMS49503.2020.00111.
Devalla, S. K., Renukanand, P. K., Sreedhar, B. K., Subramanian, G., Zhang, L., Perera, S., … Girard, M. J. A. (2018). DRUNET: a dilated-residual U-Net deep learning network to segment optic nerve head tissues in optical coherence tomography images. Biomedical Optics Express, 9(7), 3244. https://doi.org/10.1364/boe.9.003244.
Noori, M., Bahri, A., & Mohammadi, K. (2019). Attention-guided version of 2D UNet for automatic brain tumor segmentation. 2019 9th International Conference on Computer and Knowledge Engineering, ICCKE 2019, 269–275. https://doi.org/10.1109/ICCKE48569.2019.8964956.
Kumar, K. S., & Rajendran, A. (2023). Deep Convolutional Neural Network for Brain Tumor Segmentation. Journal of Electrical Engineering and Technology. https://doi.org/10.1007/s42835-023-01479-y.
Awasthi, N., Pardasani, R., & Gupta, S. (2021). Multi-threshold Attention U-Net (MTAU) Based Model for Multimodal Brain Tumor Segmentation in MRI Scans. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12659 LNCS, 168–178. https://doi.org/10.1007/978-3-030-72087-2_15.
Yadav, S. S., & Jadhav, S. M. (2019). Deep convolutional neural network based medical image classification for disease diagnosis. Journal of Big Data, 6(1). https://doi.org/10.1186/s40537-019-0276-2.
Anand, V. K., Grampurohit, S., Aurangabadkar, P., Kori, A., Khened, M., Bhat, R. S., & Krishnamurthi, G. (2021). Brain Tumor Segmentation and Survival Prediction Using Automatic Hard Mining in 3D CNN Architecture. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12659 LNCS, 310–319. https://doi.org/10.1007/978-3-030-72087-2_27.
Zhou, Z., He, Z., & Jia, Y. (2020). AFPNet: A 3D fully convolutional neural network with atrous-convolution feature pyramid for brain tumor segmentation via MRI images. Neurocomputing, 402, 235–244. https://doi.org/10.1016/j.neucom.2020.03.097.
Liu, P., Dou, Q., Wang, Q., & Heng, P. A. (2020). An encoder-decoder neural network with 3D squeeze-and-excitation and deep supervision for brain tumor segmentation. IEEE Access, 8, 34029–34037. https://doi.org/10.1109/ACCESS.2020.2973707
Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., & Maier-hein, K. H. (2017). Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge.
Alqazzaz, S. (2019). Automated brain tumor segmentation on multi-modal MR image using SegNet, 5(2), 209–219.
Chen, L., Wu, Y., Dsouza, A. M., Abidin, A. Z., & Wism, A. (n.d.). MRI Tumor Segmentation with Densely Connected 3D CNN.
Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., … Van Leemput, K. (2015). The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE Transactions on Medical Imaging, 34(10), 1993–2024. https://doi.org/10.1109/TMI.2014.2377694.
Shalabi, L. Al, Shaaban, Z., & Kasasbeh, B. (2006). Data Mining: A Preprocessing Engine. Journal of Computer Science, 2(9), 735–739. https://doi.org/10.3844/jcssp.2006.735.739.
Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on Image Data Augmentation for Deep Learning. Journal of Big Data, 6(1). https://doi.org/10.1186/s40537-019-0197-0.
Sambath Kumar, K., & Rajendran, A. (2021). An automatic brain tumor segmentation using modified inception module based U-Net model. Journal of Intelligent & Fuzzy Systems, 42(3), 2743–2754. https://doi.org/10.3233/jifs-211879.
Bertels, J., Eelbode, T., Berman, M., Vandermeulen, D., Maes, F., Bisschops, R., & Blaschko, M. B. (2019). Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory and Practice. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11765 LNCS, 92–100. https://doi.org/10.1007/978-3-030-32245-8_11.
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J. S., … Davatzikos, C. (2017). Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. Scientific Data, 4(July), 1–13. https://doi.org/10.1038/sdata.2017.117
Kingma, D. P., & Ba, J. L. (2015). Adam: A method for stochastic optimization. 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, 1–15
Dong, H., Supratak, A., Mai, L., Liu, F., Oehmichen, A., Yu, S., & Guo, Y. (2017). TensorLayer: A versatile library for efficient deep learning development. MM 2017 - Proceedings of the 2017 ACM Multimedia Conference, (1), 1201–1204. https://doi.org/10.1145/3123266.3129391.
K. S. Raju, S. Arvind, R. Chegoni, V. A. Naryana, A. Vivekananda and C. R. Kishore Babu, "Assessment of 3D MRI Image segmentation and Classification for Brain Tumor Detection Using ConvLSTM," 2023 IEEE 5th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), Hamburg, Germany, 2023, pp. 665-670, doi: 10.1109/ICCCMLA58983.2023.10346772.
Rinesh, S., K. Maheswari, B. Arthi, P. Sherubha, A. Vijay, S. Sridhar, T. Rajendran, and Yosef Asrat Waji. "Investigations on brain tumor classification using hybrid machine learning algorithms." Journal of Healthcare Engineering 2022 (2022).
Muni, T. Vijay, Ravi Kumar Tata, Jonnadula Narasimharao, K. Murali, and Harpreet Kaur. "Deep Learning Techniques for Speech Emotion Recognition." In 2022 International Conference on Futuristic Technologies (INCOFT), pp. 1-5. IEEE, 2022.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
