Unraveling the Heterogeneity of Lower-Grade Gliomas: Deep Learning-Assisted Flair Segmentation and Genomic Analysis of Brain MR Images

Authors

DOI:

https://doi.org/10.4108/eetpht.9.4016

Keywords:

Lower-grade gliomas, Brain MR images, FLAIR abnormalities, Deep learning models, Segmentation, DeepLabv3, UNet, DenseNet121Unet, ResNet50, EfficientNet, CCE, WCE, WMDL, Border region artifacts, 3D image patches, The Cancer Image Archive, TCIA, Genomic cluster data

Abstract

The precise identification of FLAIR abnormalities in brain MR images is essential for diagnosing and managing lower-grade gliomas, segmentation continues to be a difficult task. In this research, we develop an exhaustive strategy that integrates advanced deep learning models such as DeepLabv3, U-Net, DenseNet121-Unet, ResNet50, Attention U-Net and EfficientNet to effectively segment FLAIR abnormalities in a dataset comprising 110 lower-grade glioma patients. The cancer Imaging achieve (TCIA), includes genomic cluster data and patient-specific details. Our methodology tackles the multi-class data imbalanced by employing a customized loss function, which merges Categorical Cross Entropy (CCE) WCE and WMDL functions are used to calculate loss, allowing the network to accurately segment smaller tumor regions. By performing dense network training on 3D picture patches, the suggested technique improves detection of border region artifacts and efficiently manages storage and system limited resources. We evaluate our strategy’s effectiveness on the presented dataset, emphasizing its potential for assisting correct diagnosis and individualized treatment strategies for patients with lower-grade gliomas.

Downloads

Download data is not yet available.

References

Yang, H.-Y., Wang, X.-Y., Wang, Q.-Y., & Zhang, X.-J. (2012). LS-SVM based image segmentation using color and texture information. Journal of Visual Communication and Image Representation, 23(7), 1095–1112. https://doi.org/10.1016/j.jvcir.2012.07.007 DOI: https://doi.org/10.1016/j.jvcir.2012.07.007

Suresh Kumar, R., Nagaraj, B., Manimegalai, P., & Ajay, P. (2022). Dual feature extraction based convolutional neural network classifier for magnetic resonance imaging tumor detection using U-Net and three-dimensional convolutional neural network. Computers & Electrical Engineering, 101, 108010. https://doi.org/10.1016/j.compeleceng.2022.108010 DOI: https://doi.org/10.1016/j.compeleceng.2022.108010

Khorasani, A., Kafieh, R., Saboori, M., & Tavakoli, M. B. (2022). Glioma segmentation with DWI weighted images, conventional anatomical images, and post-contrast enhancement magnetic resonance imaging images by U-Net. Physical and Engineering Sciences in Medicine, 45(3), 925–934. https://doi.org/10.1007/s13246-022-01164-w DOI: https://doi.org/10.1007/s13246-022-01164-w

Sohail, N., Anwar, S. M., Majeed, F., Sanin, C., & Szczerbicki, E. (2021). Smart Approach for Glioma Segmentation in Magnetic Resonance Imaging using Modified Convolutional Network Architecture (U-NET). Cybernetics and Systems, 52(5), 445–460. https://doi.org/10.1080/01969722.2020.1871231 DOI: https://doi.org/10.1080/01969722.2020.1871231

Li, N., & Ren, K. (2021). Double attention U-Net for brain tumor MR image segmentation. International Journal of Intelligent Computing and Cybernetics, 14(3), 467–479. https://doi.org/10.1108/IJICC-01-2021-0018 DOI: https://doi.org/10.1108/IJICC-01-2021-0018

Tan, L., Ma, W., Xia, J., & Sarker, S. (2021). Multimodal Magnetic Resonance Image Brain Tumor Segmentation Based on ACU-Net Network. IEEE Access, 9, 14608–14618. https://doi.org/10.1109/ACCESS.2021.3052514 DOI: https://doi.org/10.1109/ACCESS.2021.3052514

Kihira, S., Mei, X., Mahmoudi, K., Liu, Z., Dogra, S., Belani, P., Tsankova, N., Hormigo, A., Fayad, Z. A., Doshi, A., & Nael, K. (2022). U-Net Based Segmentation and Characterization of Gliomas. Cancers, 14(18), 4457. https://doi.org/10.3390/cancers14184457 DOI: https://doi.org/10.3390/cancers14184457

Dong, H., Yang, G., Liu, F., Mo, Y., & Guo, Y. (2017). Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks. In Medical Image Understanding and Analysis (Vol. 723, pp. 506–517). Springer International Publishing AG. https://doi.org/10.1007/978-3-319-60964-5_44 DOI: https://doi.org/10.1007/978-3-319-60964-5_44

Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. arXiv.org. DOI: https://doi.org/10.1007/978-3-319-24574-4_28

Çiçek, Özgün, Abdulkadir, A., Lienkamp, S. S., Brox, T., & Ronneberger, O. (2016). 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016 (pp. 424–432). Springer International Publishing. https://doi.org/10.1007/978-3-319-46723-8_49 DOI: https://doi.org/10.1007/978-3-319-46723-8_49

Isensee, F., Schell, M., Pflueger, I., Brugnara, G., Bonekamp, D., Neuberger, U., Wick, A., Schlemmer, H., Heiland, S., Wick, W., Bendszus, M., Maier‐Hein, K. H., & Kickingereder, P. (2019). Automated brain extraction of multisequence MRI using artificial neural networks. Human Brain Mapping, 40(17), 4952–4964. https://doi.org/10.1002/hbm.24750 DOI: https://doi.org/10.1002/hbm.24750

Kamnitsas, K., Ledig, C., Newcombe, V. F. J., Simpson, J. P., Kane, A. D., Menon, D. K., Rueckert, D., & Glocker, B. (2017). Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Medical Image Analysis, 36, 61–78. https://doi.org/10.1016/j.media.2016.10.004 DOI: https://doi.org/10.1016/j.media.2016.10.004

Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.-M., & Larochelle, H. (2017). Brain tumor segmentation with Deep Neural Networks. Medical Image Analysis, 35, 18–31. https://doi.org/10.1016/j.media.2016.05.004 DOI: https://doi.org/10.1016/j.media.2016.05.004

Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J. S., Freymann, J. B., Farahani, K., & Davatzikos, C. (2017). Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. Scientific Data, 4(1), 170117–170117. https://doi.org/10.1038/sdata.2017.117 DOI: https://doi.org/10.1038/sdata.2017.117

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 DOI: https://doi.org/10.1109/TMI.2016.2538465

Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., Lanczi, L., Gerstner, E., Weber, M.-A., Arbel, T., Avants, B. B., Ayache, N., Buendia, P., Collins, D. L., Cordier, N., … Delingette, H. (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 DOI: https://doi.org/10.1109/TMI.2014.2377694

Zhou, Z., Md Mahfuzur Rahman Siddiquee, Tajbakhsh, N., & Liang, J. (2018). UNet++: A Nested U-Net Architecture for Medical Image Segmentation. arXiv.org. DOI: https://doi.org/10.1007/978-3-030-00889-5_1

Krizhevsky, A., Sutskever, I., & Hinton, G. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84–90. https://doi.org/10.1145/3065386 DOI: https://doi.org/10.1145/3065386

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature (London), 521(7553), 436–444. https://doi.org/10.1038/nature14539 DOI: https://doi.org/10.1038/nature14539

Simonyan, K., & Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv.org.

Kaiming He, Xiangyu Zhang, Shaoqing Ren, & Jian Sun. (2016). Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778. https://doi.org/10.1109/CVPR.2016.90 DOI: https://doi.org/10.1109/CVPR.2016.90

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Dumitru Erhan, Vanhoucke, V., & Rabinovich, A. (2014). Going Deeper with Convolutions. arXiv.org. DOI: https://doi.org/10.1109/CVPR.2015.7298594

Ioffe, S., & Szegedy, C. (2015). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. https://doi.org/10.48550/arxiv.1502.03167

Hurtado Oliver, L. F., González-Barba, J. Ángel, & Pla Santamaría, F. (2019). Choosing the right loss function for multi-label Emotion Classification. DOI: https://doi.org/10.3233/JIFS-179019

Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. https://doi.org/10.48550/arxiv.1207.0580

Kingma, D. P., & Ba, J. (2014). Adam: A Method for Stochastic Optimization. https://doi.org/10.48550/arxiv.1412.6980

Dice, L. R. (1945). Measures of the Amount of Ecologic Association Between Species. Ecology (Durham), 26(3), 297–302. https://doi.org/10.2307/1932409

Tustison, N. J., Avants, B. B., Cook, P. A., Yuanjie Zheng, Egan, A., Yushkevich, P. A., & Gee, J. C. (2010). N4ITK: Improved N3 Bias Correction. IEEE Transactions on Medical Imaging, 29(6), 1310–1320. https://doi.org/10.1109/TMI.2010.2046908 DOI: https://doi.org/10.1109/TMI.2010.2046908

Zou, K. H., Warfield, S. K., Bharatha, A., Tempany, C. M. C., Kaus, M. R., Haker, S. J., Wells, W. M., Jolesz, F. A., & Kikinis, R. (2004). Statistical validation of image segmentation quality based on a spatial overlap index scientific report. Academic Radiology, 11(2), 178–189. https://doi.org/10.1016/S1076-6332(03)00671-8 DOI: https://doi.org/10.1016/S1076-6332(03)00671-8

Dice, L. R. (1945). Measures of the Amount of Ecologic Association Between Species. Ecology (Durham), 26(3), 297–302. https://doi.org/10.2307/1932409 DOI: https://doi.org/10.2307/1932409

Ensemble Learning Framework with GLCM Texture Extraction for Early Detection of Lung Cancer on CT Images, Sara A Althubiti, Sanchita Paul, Rajnikanta Mohanty, Sachi Nandan Mohanty, Fayadh Alenezi, Kemal Polat, Computational and Mathematical Methods in Medicine (Hindawi), 2022, doi.org/10.1155/2022/2733965 DOI: https://doi.org/10.1155/2022/2733965

A Novel Approach for Diabetic Retinopathy Screening Using Asymmetric Deep Learning Feature, Pradeep Kumar Jena, Bonomali Khuntia, Charulata Palai, Manjushree Nayak, Tapas Kumar Mishra, Sachi Nandan Mohanty, Big Data Cognitive Computing (2023), Vol 7, Issue 1, 25, https://doi.org/10.3390/bdcc7010025, ISSN: 2504-2289 DOI: https://doi.org/10.3390/bdcc7010025

Downloads

Published

29-09-2023

How to Cite

1.
Rahat IS, Ghosh H, Shaik K, Khasim S, Rajaram G. Unraveling the Heterogeneity of Lower-Grade Gliomas: Deep Learning-Assisted Flair Segmentation and Genomic Analysis of Brain MR Images. EAI Endorsed Trans Perv Health Tech [Internet]. 2023 Sep. 29 [cited 2024 May 25];9. Available from: https://publications.eai.eu/index.php/phat/article/view/4016