DETECTING ABNORMAL AREAS ON BRAIN MRI IMAGES WITH SWIN-UNET

Lê Minh Lợi; Trần Nguyễn Minh Thư; Hồ Quốc An; Phạm Nguyên Khang

doi:10.34238/tnu-jst.10053

DETECTING ABNORMAL AREAS ON BRAIN MRI IMAGES WITH SWIN-UNET

About this article

Received: 07/04/24 Revised: 10/06/24 Published: 10/06/24

Authors

1. Le Minh Loi, 1) Can Tho University of Medicine and Pharmacy, 2) Can Tho University
2. Tran Nguyen Minh Thu , Can Tho University
3. Ho Quoc An, Can Tho University
4. Pham Nguyen Khang, Can Tho University

Abstract

To identify abnormal areas on brain MRI images, radiologists need to examine many slices from the image set. This research helps automatically suggest abnormal areas of the brain on MRI images. The Unet, ResNet, Swin-Unet models are trained on the Can Tho University of Medicine and Pharmacy Hospital data set combined with the LGG data set to segment images with or without abnormal regions. The model will then suggest the abnormal region through the boundary drawn around it. Experimental results show that, when dividing random data by image, the Swin-Unet model achieves the highest accuracy with 0.88, along with Recall, Precision and F1 Score of 0.96, 0.71, and 0.82 respectively. For determining the location and shape of the abnormal region, Swin-Unet also demonstrated high performance with mIoU reaching 0.89 and mDSC reaching 0.91. When dividing the data by patient, the Swin-Unet model once again showed good performance with Accuracy reaching 0.86, along with Recall of 0.88, Precision of 0.79, F1 Score of 0.83, and for mIoU it achieved 0.84 and mDSC reached 0.89. Research results show that the Swin-Unet model has good results in the problem of detecting abnormal areas on brain MRI images.

Keywords

Detecting abnormalities; Medical image segmentation; Deep learning; Transformer; SwinUnet

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References

[1] Tsai, A. Yezzi, W. Wells, C. Tempany, D. Tucker, A. Fan, W. Grimson, and A. Willsky, “A shape-based approach to the segmentation of medical imagery using level sets,” IEEE Transactions on Medical Imaging, vol. 22, no. 2, pp. 137–154, 2003.

[2] K. Held, E. Kops, B. Krause, W. Wells, R. Kikinis, and H.-W. Muller-Gartner, “Markov random field segmentation of brain mr images,” IEEE Transactions on Medical Imaging, vol. 16, no. 6, pp. 878–886, 1997.

[3] M. L. Le, N. M. T. Tran, T. N. Ho, and N. K. Pham, “Using U-Net model to detect abnormal areas on brain MRI images,” The National Conference on Fundamental and Applied IT Research (in Vietnamese) (FAIR), Nha Trang, 2020, pp. 656-662.

[4] O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks forbiomedical image segmentation,” in Medical Image Computing and ComputerAssisted Intervention (MICCAI), ser. LNCS, vol. 9351, Springer, 2015, pp. 234– 241

[5] X. Xiao, S. Lian, Z. Luo, and S. Li, “Weighted res-unet for high-quality retina vesselsegmentation,” 2018 9th International Conference on Information Technology in Medicine and Education (ITME), 2018, pp. 327–331.

[6] Z. Zhou, M. R. Siddiquee, N. Tajbakhsh, and J. Liang, Unet++: A nestedu-net architecture for medical image segmentation. Springer Verlag, 2018, pp. 3–11.

[7] X. Li, H. Chen, X. Qi, Q. Dou, C.-W. Fu, and P.-A. Heng, “H-denseunet: Hybriddensely connected unet for liver and tumor segmentation from ct volumes,” IEEE Transactions on Medical Imaging, vol. 37, no. 12, pp. 2663–2674, 2018.

[8] O. C¸ic¸ek, A. Abdulkadir, S. Lienkamp, T. Brox, and O. Ronneberger, “3d u-net:¨ Learning dense volumetric segmentation from sparse annotation,” in Medical Image Computing and Computer-Assisted Intervention (MICCAI), ser. LNCS, vol. 9901, Springer, Oct. 2016, pp. 424–432.

[9] N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” CoRR, 2020, doi: 10.48550/arXiv.2005.12872.

[10] Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby, “An image is worth 16x16 words: Transformers for image recognition at scale,” in International Conference on Learning Representations, arXiv preprint arXiv:2010.11929, 2020.

[11] A. Hatamizadeh, V. Nath, Y. Tang, D. Yang, H. R. Roth, and D. Xu, “Swin UnetR: Swin Transformers for Segmantic Segmentation of Brain Tumors in MRI Images,” in Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Springer International Publishing, 2022, pp.272-284.

[12] H. Cao, Y. Wang, J. Chen, D. S. Jiang, X. P. Zhang, Q. Tian, and M. Wang, “Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation,” in European conference on computer vision. Cham: Springer Nature Switzerland, 2022, pp. 205-218.

[13] Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo, “Swintransformer: Hierarchical vision transformer using shifted windows,” in 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada, 2021, pp. 9992-10002.

[14] N. M. T. Tran, T. L. Bui, M. L. Le, T. N. Phu, and N. K. Pham, “Applying CGAN model for detecting abnormal areas on brain MRI images,” The National Conference on Fundamental and Applied IT Research (in Vietnamese) (FAIR), Ha Noi, 2022, pp. 462-469.

DOI: https://doi.org/10.34238/tnu-jst.10053

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