UniMRI: Unified Repository of Magnetic Resonance Images for Multiple Sclerosis Diagnosis
Resumo
Multiple sclerosis is an autoimmune disease that affects the central nervous system, destroying myelin. To detect multiple sclerosis, you need to have MRI scans so you can see the areas where myelin has been damaged. This analysis is complex and costly due to the time required to assess injuries. The use of machine learning is desirable as these exams are taken periodically. However, the number of public databases present in the literature containing patients with multiple sclerosis is small when compared to the amount of data needed to train deep neural networks. Thus, the objective of this work is to join public databases of magnetic resonance images existing in the literature, proposing a software library to manipulate and pre-process these data.
Palavras-chave:
multiple sclerosis, magnetic resonance images, visual dataset, medical imaging
Referências
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A. A. Mazzola, “Ressonância magnética: princípios de formação da imagem e aplicações em imagem funcional,” Revista Brasileira de Física Médica, vol. 3, no. 1, pp. 117-129, 2009.
P. J. LaMontagne, T. L. Benzinger, J. C. Morris, S. Keefe, R. Hornbeck, C. Xiong, E. Grant, J. Hassenstab, K. Moulder, A. Vlassenko et al., “Oasis-3: longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and alzheimer disease,” MedRxiv, 2019.
B. A. Landman, A. J. Huang, A. Gifford, D. S. Vikram, I. A. L. Lim, J. A. Farrell, J. A. Bogovic, J. Hua, M. Chen, S. Jarso et al., “Multi-parametric neuroimaging reproducibility: a 3-t resource study,” Neuroimage, vol. 54, no. 4, pp. 2854-2866, 2011.
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O. Commowick, F. Cervenansky, and R. Ameli, “Msseg challenge proceedings: multiple sclerosis lesions segmentation challenge using a data management and processing infrastructure,” in Miccai, 2016.
ˇ Z. Lesjak, A. Galimzianova, A. Koren, M. Lukin, F. Pernuˇs, B. Likar, and ˇ Z. ˇ Spiclin, “A novel public mr image dataset of multiple sclerosis patients with lesion segmentations based on multi-rater consensus,” Neuroinformatics, vol. 16, no. 1, pp. 51-63, 2018.
A. Carass, S. Roy, A. Jog, J. L. Cuzzocreo, E. Magrath, A. Gherman, J. Button, J. Nguyen, F. Prados, C. H. Sudre et al., “Longitudinal multiple sclerosis lesion segmentation: resource and challenge,” NeuroImage, vol. 148, pp. 77-102, 2017.
eHealth Lab, Department of Computer Science University of Cyprus, “Mri lesion segmentation in multiple sclerosis database,” http://ehealthlab.cs.ucy.ac.cy/index.php/facilities/32-software/218-datasets, 2021.
M. Kocak, “Ressonância magnética,” [link], 2019.
D. C. Preston, “Magnetic resonance imaging (mri) of the brain and spine: Basics,” https://case.edu/med/neurology/NR/MRI%20Basics.htm, 2006.
P. C. T. Gomes, “Conheça as etapas do pré-processamento de dados,” https://www.datageeks.com.br/pre-processamento-de-dados/, 2019.
J. E. C. Castanho and C. L. Tozzi, “Obtenção da transformação de corpo rígido em registro de imagens com deformação usando thin plate splines,” Simpósio Brasileiro de Computação Gráfica e Processamento de Imagens. Anais. Caxambu, 1996.
D. Tosun-Turgut, “Rigid image registration,” [link], 2016.
J. O. Giacomantone, “Ressonância magnética funcional com filtragem pela difusão anisotrópica robusta.” Ph.D. dissertation, Universidade de São Paulo, 2005.
P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Transactions on pattern analysis and machine intelligence, vol. 12, no. 7, pp. 629-639, 1990.
J. Juntu, J. Sijbers, D. Van Dyck, and J. Gielen, “Bias field correction for mri images,” in Computer recognition systems. Springer, 2005, pp. 543-551.
J. G. Sled, A. P. Zijdenbos, and A. C. Evans, “A nonparametric method for automatic correction of intensity nonuniformity in mri data,” IEEE transactions on medical imaging, vol. 17, no. 1, pp. 87-97, 1998.
J. E. Iglesias, C.-Y. Liu, P. M. Thompson, and Z. Tu, “Robust brain extraction across datasets and comparison with publicly available methods,” IEEE transactions on medical imaging, vol. 30, no. 9, pp. 1617- 1634, 2011.
O'Dwyer, “Api mega,” https://github.com/odwyersoftware/mega.py, 2020.
PyTorch, “Framework pytorch,” https://pytorch.org/, 2021.
Multiple Sclerosis International Federation - MSIF, “Atlas of ms,” https://www.atlasofms.org/map/global/epidemiology/number-of-people-with-ms, 2020.
A. Teixeira, “Tudo sobre células-tronco e esclerose múltipla,” [link], 2017.
A. A. Mazzola, “Ressonância magnética: princípios de formação da imagem e aplicações em imagem funcional,” Revista Brasileira de Física Médica, vol. 3, no. 1, pp. 117-129, 2009.
P. J. LaMontagne, T. L. Benzinger, J. C. Morris, S. Keefe, R. Hornbeck, C. Xiong, E. Grant, J. Hassenstab, K. Moulder, A. Vlassenko et al., “Oasis-3: longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and alzheimer disease,” MedRxiv, 2019.
B. A. Landman, A. J. Huang, A. Gifford, D. S. Vikram, I. A. L. Lim, J. A. Farrell, J. A. Bogovic, J. Hua, M. Chen, S. Jarso et al., “Multi-parametric neuroimaging reproducibility: a 3-t resource study,” Neuroimage, vol. 54, no. 4, pp. 2854-2866, 2011.
M. Styner, J. Lee, B. Chin, M. Chin, O. Commowick, H. Tran, S. Markovic-Plese, V. Jewells, and S. Warfield, “3d segmentation in the clinic: A grand challenge ii: Ms lesion segmentation,” Midas Journal, vol. 2008, pp. 1-6, 2008.
O. Commowick, F. Cervenansky, and R. Ameli, “Msseg challenge proceedings: multiple sclerosis lesions segmentation challenge using a data management and processing infrastructure,” in Miccai, 2016.
ˇ Z. Lesjak, A. Galimzianova, A. Koren, M. Lukin, F. Pernuˇs, B. Likar, and ˇ Z. ˇ Spiclin, “A novel public mr image dataset of multiple sclerosis patients with lesion segmentations based on multi-rater consensus,” Neuroinformatics, vol. 16, no. 1, pp. 51-63, 2018.
A. Carass, S. Roy, A. Jog, J. L. Cuzzocreo, E. Magrath, A. Gherman, J. Button, J. Nguyen, F. Prados, C. H. Sudre et al., “Longitudinal multiple sclerosis lesion segmentation: resource and challenge,” NeuroImage, vol. 148, pp. 77-102, 2017.
eHealth Lab, Department of Computer Science University of Cyprus, “Mri lesion segmentation in multiple sclerosis database,” http://ehealthlab.cs.ucy.ac.cy/index.php/facilities/32-software/218-datasets, 2021.
M. Kocak, “Ressonância magnética,” [link], 2019.
D. C. Preston, “Magnetic resonance imaging (mri) of the brain and spine: Basics,” https://case.edu/med/neurology/NR/MRI%20Basics.htm, 2006.
P. C. T. Gomes, “Conheça as etapas do pré-processamento de dados,” https://www.datageeks.com.br/pre-processamento-de-dados/, 2019.
J. E. C. Castanho and C. L. Tozzi, “Obtenção da transformação de corpo rígido em registro de imagens com deformação usando thin plate splines,” Simpósio Brasileiro de Computação Gráfica e Processamento de Imagens. Anais. Caxambu, 1996.
D. Tosun-Turgut, “Rigid image registration,” [link], 2016.
J. O. Giacomantone, “Ressonância magnética funcional com filtragem pela difusão anisotrópica robusta.” Ph.D. dissertation, Universidade de São Paulo, 2005.
P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Transactions on pattern analysis and machine intelligence, vol. 12, no. 7, pp. 629-639, 1990.
J. Juntu, J. Sijbers, D. Van Dyck, and J. Gielen, “Bias field correction for mri images,” in Computer recognition systems. Springer, 2005, pp. 543-551.
J. G. Sled, A. P. Zijdenbos, and A. C. Evans, “A nonparametric method for automatic correction of intensity nonuniformity in mri data,” IEEE transactions on medical imaging, vol. 17, no. 1, pp. 87-97, 1998.
J. E. Iglesias, C.-Y. Liu, P. M. Thompson, and Z. Tu, “Robust brain extraction across datasets and comparison with publicly available methods,” IEEE transactions on medical imaging, vol. 30, no. 9, pp. 1617- 1634, 2011.
O'Dwyer, “Api mega,” https://github.com/odwyersoftware/mega.py, 2020.
PyTorch, “Framework pytorch,” https://pytorch.org/, 2021.
Publicado
22/11/2021
Como Citar
SILVEIRA, Wellington; KORB, Rafael; DIMURO, Graçaliz; BEM, Rodrigo de.
UniMRI: Unified Repository of Magnetic Resonance Images for Multiple Sclerosis Diagnosis. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 17. , 2021, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 190-194.
DOI: https://doi.org/10.5753/wvc.2021.18912.
