Zero-Shot Skull Stripping for Alzheimer’s Disease Classification With the Segment Anything Model
Resumo
This study explores magnetic resonance imaging segmentation for Alzheimer’s disease by automating skull stripping using the Segment Anything Model (SAM), a zero-shot segmentation model. The challenge lies in selecting the correct mask generated by the SAM, for which we propose a heuristic based on templates to identify the optimal choice. This method presents a practical alternative to the traditional FMRIB Software Library Brain Extraction Tool. The effectiveness of our approach is indirectly assessed using Alzheimer’s disease classification as a proxy task. Validation is conducted using the Alzheimer’s Disease Neuroimaging Initiative dataset, demonstrating a 6% improvement in classification accuracy with the zero-shot approach.
Palavras-chave:
Zero-shot segmentation, Alzheimer's disease, MRI skull stripping
Referências
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El-Baz, S., Soliman, F., and Gimel’farb, G. (2016). A survey of brain mri image segmentation methods and the issues involved. Magnetic Resonance Imaging, 34(10):1300–1318.
Farooq, A., Anwar, S., Awais, M., and Rehman, S. (2017). A deep cnn based multi-class classification of alzheimer’s disease using mri. In 2017 IEEE International Conference on Imaging systems and techniques (IST), 1–6. IEEE.
Fatima, A., Shahid, A. R., Raza, B., Madni, T. M., and Janjua, U. I. (2020). State-of-the-art traditional to the machine-and deep-learning-based skull stripping techniques, models, and algorithms. Journal of Digital Imaging, 33:1443–1464.
Gunawardena, K., Rajapakse, R., and Kodikara, N. D. (2017). Applying convolutional neural networks for pre-detection of alzheimer’s disease from structural mri data. In 2017 24th international conference on mechatronics and machine vision in practice (M2VIP), 1–7. IEEE.
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., and Girshick, R. (2021). Masked autoencoders are scalable vision learners.
Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W., and Smith, S. M. (2012). Fsl. Neuroimage, 62(2):782–790.
Kalavathi, P. and Prasath, V. S. (2016). Methods on skull stripping of mri head scan images—a review. Journal of digital imaging, 29:365–379.
Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.-Y., et al. (2023). Segment anything. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 4015–4026.
Kleesiek, J., Urban, G., Hubert, A., Schwarz, D., Maier-Hein, K., Bendszus, M., and Biller, A. (2016). Deep mri brain extraction: A 3d convolutional neural network for skull stripping. NeuroImage, 129:460–469.
Lee, G., Nho, K., Kang, B., et al. (2019). Predicting alzheimer’s disease progression using multi-modal deep learning approach. Scientific Reports.
Luo, S., Li, X., and Li, J. (2017). Automatic alzheimer’s disease recognition from mri data using deep learning method. Journal of Applied Mathematics and Physics, 5(9):1892–1898.
Mofrad, S. A., Lundervold, A. J., Vik, A., et al. (2021). Cognitive and mri trajectories for prediction of alzheimer’s disease. Scientific Reports, 11:2122.
Mohapatra, S., Gosai, A., and Schlaug, G. (2023). Sam vs bet: A comparative study for brain extraction and segmentation of magnetic resonance images using deep learning.
Oh, K., Chung, Y. C., Kim, K. W., et al. (2019). Classification and visualization of alzheimer’s disease using volumetric convolutional neural network and transfer learning. Scientific Reports, 9:18150.
Pei, L., Ak, M., Tahon, N. H., Zenkin, S., Alkarawi, S., Kamal, A., Yilmaz, M., Chen, L., Er, M., Ak, N., and Colen, R. (2022). A general skull stripping of multiparametric brain mris using 3d convolutional neural network. Scientific Reports, 12:10826.
Perlaki, G., Horvath, R., Nagy, S. A., Bogner, P., Doczi, T., Janszky, J., and Orsi, G. (2017). Comparison of accuracy between fsl’s first and freesurfer for caudate nucleus and putamen segmentation. Scientific Reports, 7(1):2418.
Petersen, R. C., Aisen, P. S., Beckett, L. A., Donohue, M. C., Gamst, A. C., Harvey, D. J., Jack Jr, C., Jagust, W. J., Shaw, L. M., Toga, A. W., et al. (2010). Alzheimer’s disease neuroimaging initiative (adni) clinical characterization. Neurology, 74(3):201–209.
Quilis-Sancho, J., Fernandez-Blazquez, M. A., and Gomez-Ramirez, J. (2020). A comparative analysis of automated mri brain segmentation in a large longitudinal dataset: Freesurfer vs. fsl. bioRxiv.
Scheltens, P., Leys, D., Barkhof, F., Huglo, D., Weinstein, H. C., Vermersch, P., Kuiper, M., Steinling, M., Wolters, E. C., and Valk, J. (1992). Atrophy of medial temporal lobes on MRI in “probable” alzheimer’s disease and normal ageing: diagnostic value and neuropsychological correlates. J Neurol Neurosurg Psychiatry, 55(10):967–972.
Shi, J., Zheng, X., Li, Y., Zhang, Q., and Ying, S. (2018). Multimodal neuroimaging feature learning with multimodal stacked deep polynomial networks for diagnosis of alzheimer’s disease. IEEE Journal of Biomedical and Health Informatics, 22(1):173–183.
Smith, S. M. (2002). Fast robust automated brain extraction. Hum. Brain Mapp., 17(3):143–155.
Suk, H.-I., Lee, S.-W., and Shen, D. (2014). Hierarchical feature representation and multimodal fusion with deep learning for ad/mci diagnosis. NeuroImage, 101:569–582.
Varoquaux, G. and Cheplygina, V. (2022). Machine learning for medical imaging: methodological failures and recommendations for the future. npj Digital Medicine, 5(1):48.
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N. (2021). An image is worth 16x16 words: Transformers for image recognition at scale.
El-Baz, S., Soliman, F., and Gimel’farb, G. (2016). A survey of brain mri image segmentation methods and the issues involved. Magnetic Resonance Imaging, 34(10):1300–1318.
Farooq, A., Anwar, S., Awais, M., and Rehman, S. (2017). A deep cnn based multi-class classification of alzheimer’s disease using mri. In 2017 IEEE International Conference on Imaging systems and techniques (IST), 1–6. IEEE.
Fatima, A., Shahid, A. R., Raza, B., Madni, T. M., and Janjua, U. I. (2020). State-of-the-art traditional to the machine-and deep-learning-based skull stripping techniques, models, and algorithms. Journal of Digital Imaging, 33:1443–1464.
Gunawardena, K., Rajapakse, R., and Kodikara, N. D. (2017). Applying convolutional neural networks for pre-detection of alzheimer’s disease from structural mri data. In 2017 24th international conference on mechatronics and machine vision in practice (M2VIP), 1–7. IEEE.
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., and Girshick, R. (2021). Masked autoencoders are scalable vision learners.
Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W., and Smith, S. M. (2012). Fsl. Neuroimage, 62(2):782–790.
Kalavathi, P. and Prasath, V. S. (2016). Methods on skull stripping of mri head scan images—a review. Journal of digital imaging, 29:365–379.
Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.-Y., et al. (2023). Segment anything. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 4015–4026.
Kleesiek, J., Urban, G., Hubert, A., Schwarz, D., Maier-Hein, K., Bendszus, M., and Biller, A. (2016). Deep mri brain extraction: A 3d convolutional neural network for skull stripping. NeuroImage, 129:460–469.
Lee, G., Nho, K., Kang, B., et al. (2019). Predicting alzheimer’s disease progression using multi-modal deep learning approach. Scientific Reports.
Luo, S., Li, X., and Li, J. (2017). Automatic alzheimer’s disease recognition from mri data using deep learning method. Journal of Applied Mathematics and Physics, 5(9):1892–1898.
Mofrad, S. A., Lundervold, A. J., Vik, A., et al. (2021). Cognitive and mri trajectories for prediction of alzheimer’s disease. Scientific Reports, 11:2122.
Mohapatra, S., Gosai, A., and Schlaug, G. (2023). Sam vs bet: A comparative study for brain extraction and segmentation of magnetic resonance images using deep learning.
Oh, K., Chung, Y. C., Kim, K. W., et al. (2019). Classification and visualization of alzheimer’s disease using volumetric convolutional neural network and transfer learning. Scientific Reports, 9:18150.
Pei, L., Ak, M., Tahon, N. H., Zenkin, S., Alkarawi, S., Kamal, A., Yilmaz, M., Chen, L., Er, M., Ak, N., and Colen, R. (2022). A general skull stripping of multiparametric brain mris using 3d convolutional neural network. Scientific Reports, 12:10826.
Perlaki, G., Horvath, R., Nagy, S. A., Bogner, P., Doczi, T., Janszky, J., and Orsi, G. (2017). Comparison of accuracy between fsl’s first and freesurfer for caudate nucleus and putamen segmentation. Scientific Reports, 7(1):2418.
Petersen, R. C., Aisen, P. S., Beckett, L. A., Donohue, M. C., Gamst, A. C., Harvey, D. J., Jack Jr, C., Jagust, W. J., Shaw, L. M., Toga, A. W., et al. (2010). Alzheimer’s disease neuroimaging initiative (adni) clinical characterization. Neurology, 74(3):201–209.
Quilis-Sancho, J., Fernandez-Blazquez, M. A., and Gomez-Ramirez, J. (2020). A comparative analysis of automated mri brain segmentation in a large longitudinal dataset: Freesurfer vs. fsl. bioRxiv.
Scheltens, P., Leys, D., Barkhof, F., Huglo, D., Weinstein, H. C., Vermersch, P., Kuiper, M., Steinling, M., Wolters, E. C., and Valk, J. (1992). Atrophy of medial temporal lobes on MRI in “probable” alzheimer’s disease and normal ageing: diagnostic value and neuropsychological correlates. J Neurol Neurosurg Psychiatry, 55(10):967–972.
Shi, J., Zheng, X., Li, Y., Zhang, Q., and Ying, S. (2018). Multimodal neuroimaging feature learning with multimodal stacked deep polynomial networks for diagnosis of alzheimer’s disease. IEEE Journal of Biomedical and Health Informatics, 22(1):173–183.
Smith, S. M. (2002). Fast robust automated brain extraction. Hum. Brain Mapp., 17(3):143–155.
Suk, H.-I., Lee, S.-W., and Shen, D. (2014). Hierarchical feature representation and multimodal fusion with deep learning for ad/mci diagnosis. NeuroImage, 101:569–582.
Varoquaux, G. and Cheplygina, V. (2022). Machine learning for medical imaging: methodological failures and recommendations for the future. npj Digital Medicine, 5(1):48.
Publicado
17/11/2024
Como Citar
PINHEIRO, Jean Christopher; PINHEIRO, Vitória Chrissie de O.; SILVA, Rodrigo; MOREIRA, Gladston; SILVA, Pedro H. L.; LUZ, Eduardo J. S..
Zero-Shot Skull Stripping for Alzheimer’s Disease Classification With the Segment Anything Model. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 21. , 2024, Belém/PA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 156-167.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2024.245169.
