Automating the Koedam Parietal Atrophy Scale for Alzheimer's Using MRI Features and Clustering Techniques
Resumo
Early detection of Alzheimer’s disease (AD) is crucial for effective intervention, and imaging biomarkers are pivotal in this process. The search for imaging biomarkers is important in diagnosing AD, offering a non-invasive and potentially early method to identify brain changes associated with the disease. These biomarkers can provide valuable insights into the progression of AD and aid in differential diagnosis, enabling the application of more effective treatment strategies. In this context, the Koedam visual scale for parietal atrophy is a valuable tool for assessing structural changes in the parietal lobe associated with AD. This study proposes an automated approach for the Koedam scale using attributes extracted from T1-weighted magnetic resonance imaging (MRI) combined with clustering techniques. Initially, a preprocessing pipeline is applied to the images to skull stripping, to mitigate noise and bias field effects and to define the ROI (parietal region). Subsequently, a finite mixture model is applied to segment the images into gray matter, white matter, and cerebrospinal fluid. The volume of each tissue is then utilized as a feature for clustering, effectively simulating the visual categorization of the Koedam scale. Our method, tested on 103 MRI images, demonstrates potential for automating the assessment of parietal atrophy, providing a more objective and efficient evaluation tool.
Palavras-chave:
Koedam scale, Alzheimer’s disease, Clustering, MRI, Posterior atrophy
Referências
A. Association, “Alzheimer’s Disease Facts and Figures,” Tech. Rep. 4, 2023.
A. Atri, “The Alzheimer’s Disease Clinical Spectrum: Diagnosis and Management,” Medical Clinics, vol. 103, no. 2, pp. 263–293, 2019.
J. Smid, A. Studart-Neto, K. César-Freitas, M. Dourado, R. Kochhann, B. Barbosa, L. Schilling, M. Balthazar, N. Frota, L. de Souza, P. Caramelli, P. Bertolucci, M. Chaves, S. Bruck, R. Nitrini, E. Resende, and F. Vale, “Declínio cognitivo subjetivo, comprometimento cognitivo leve e demência – diagnóstico sindrômico: recomendações do Departamento Científico de Neurologia Cognitiva e do Envelhecimento da Academia Brasileira de Neurologia,” Dement Neuropsychol, vol. 16, no. 3, pp. 1–17, 2022.
K. Dhana, O. H. Franco, E. M. Ritz, C. N. Ford, P. Desai, K. R. Krueger, T. M. Holland, A. Dhana, X. Liu, N. T. Aggarwal, D. A. Evans, and K. B. Rajan, “Healthy lifestyle and life expectancy with and without Alzheimer’s dementia: population based cohort study,” BMJ (Clinical research ed.), vol. 377, no. e068390, 2022.
Y. Rao, B. Ganaraja, B. Murlimanju, T. Joy, A. Krishnamurthy, and A. Agrawal, “Hippocampus and its involvement in Alzheimer’s disease: a review,” 3 Biotech, vol. 12, no. 55, pp. 1–10, 2022.
B. Dubois, C. von Arnim, N. Burnie, S. Bozeat, and J. Cummings, “Biomarkers in Alzheimer’s disease: role in early and differential diagnosis and recognition of atypical variants,” Alzheimer’s Research & Therapy, vol. 15, no. 175, pp. 1–13, 2023.
X. Feng, F. Provenzano, and S. Small, “A deep learning MRI approach outperforms other biomarkers of prodromal Alzheimer’s disease,” Alzheimer’s Research & Therapy, vol. 14, no. 45, pp. 1–11, 2022.
T. Klyucherev, P. Olszewski, A. Shalimova, V. Chubarev, V. Tarasov, M. Attwood, S. Syvänen, and H. Schiöth, “Advances in the development of new biomarkers for Alzheimer’s disease,” Translational Neurodegeneration, vol. 11, no. 25, pp. 1–24, 2022.
J. Wu, K. Zhao, Z. Li, D. Wang, Y. Ding, Y. Wei, H. Zhang, and Y. Liu, “A systematic analysis of diagnostic performance for Alzheimer’s disease using structural MRI,” Psychoradiology, vol. 2, no. 1, pp. 1–9, 2022.
G. Karas, P. Scheltens, S. Rombouts, R. van Schijndel, M. Klein, B. Jones, W. van der Flier, H. Vrenken, and F. Barkhof, “Precuneus atrophy in early-onset Alzheimer’s disease: a morphometric structural MRI study,” Neuroradiology, vol. 49, no. 12, pp. 967–976, 2007.
E. Koedman, M. Lehmann, W. van der Flier, P. Scheltens, Y. Pijnenburg, N. Fox, F. Barkhof, and M. Wattjes, “Visual assessment of posterior atrophy development of a MRI rating scale,” European Radiology, vol. 21, pp. 2618–2625, 2011.
L.-O. Wahlund, P. Julin, J. Lindqvist, and P. Scheltens, “Visual assessment of medial temporal lobe atrophy in demented and healthy control subjects: correlation with volumetry,” Psychiatry Research: Neuroimaging, vol. 90, no. 3, pp. 193–199, 1999. [Online]. Available: [link]
J. E. Iglesias, C. 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.
J. Manjón, P. Coupé, L. Martí-Bonmatí, D. Collins, and M. Robles, “Adaptive non-local means denoising of MR images with spatially varying noise levels,” Journal of Magnetic Resonance Imaging, vol. 31, no. 1, pp. 192–203, 2010.
J. Manjón, J. Carbonell-Caballero, J. Lull, L. García-Martí, G. Martí-Bonmatí, and M. Robles, “MRI denoising using Non-Local Means,” Medical Image Analysis, vol. 12, pp. 514–523, 2008.
N. J. Tustison, B. B. Avants, P. A. Cook, Y. Zheng, A. Egan, P. A. Yushkevich, and J. C. Gee, “N4ITK: improved N3 bias correction,” IEEE Transactions on Medical Imaging, vol. 29, no. 6, pp. 1310–1320, 2010.
A. Manera, M. Dadar, V. Fonov, and D. Collins, “CerebrA, registration and manual label correction of Mindboggle-101 atlas for MNI-ICBM152 template,” Scientific Data, vol. 7, no. 1, pp. 1–9, 2020.
B. Avants, N. Tustison, J. Wu, P. Cook, and J. Gee, “An Open Source Multivariate Framework for n-Tissue Segmentation with Evaluation on Public Data,” Neuroinformatics, vol. 9, no. 4, pp. 381–400, 2011.
M. Rebsamen, C. Rummel, M. Reyes, R. Wiest, and R. Mckinley, “Direct cortical thickness estimation using deep learning-based anatomy segmentation and cortex parcellation,” Human Brain Mapping, vol. 41, pp. 4804–4814, 2021.
M. Mendez, “Early-Onset Alzheimer’s Disease,” Neurologic Clinics, vol. 35, no. 2, pp. 263–281, 2017.
L. Apostolova, P. Aisen, A. Eloyan, A. Fagan, K. Fargo, T. Tatiana Foroud, C. Gatsonis, L. Grinberg, C. Jack Jr., J. Kramer, R. Koeppe, W. Kukull, M. Murray, K. Nudelman, M. Rumbaugh, A. Toga, P. Vemuri, A. Trullinger, L. Iaccarino, G. Day, N. Graff-Radford, L. Honig, D. Jones, J. Masdeu, M. Mendez, E. Musiek, C. Onyike, E. Rogalski, S. Salloway, D. Wolk, T. Wingo, M. Carrillo, B. Dickerson, G. Rabinovici, and the LEADS Consortium, “The Longitudinal Early-onset Alzheimer’s Disease Study (LEADS): Framework and methodology,” Alzheimer’s & Dementia, vol. 17, pp. 2043–2055, 2021.
A. Atri, “The Alzheimer’s Disease Clinical Spectrum: Diagnosis and Management,” Medical Clinics, vol. 103, no. 2, pp. 263–293, 2019.
J. Smid, A. Studart-Neto, K. César-Freitas, M. Dourado, R. Kochhann, B. Barbosa, L. Schilling, M. Balthazar, N. Frota, L. de Souza, P. Caramelli, P. Bertolucci, M. Chaves, S. Bruck, R. Nitrini, E. Resende, and F. Vale, “Declínio cognitivo subjetivo, comprometimento cognitivo leve e demência – diagnóstico sindrômico: recomendações do Departamento Científico de Neurologia Cognitiva e do Envelhecimento da Academia Brasileira de Neurologia,” Dement Neuropsychol, vol. 16, no. 3, pp. 1–17, 2022.
K. Dhana, O. H. Franco, E. M. Ritz, C. N. Ford, P. Desai, K. R. Krueger, T. M. Holland, A. Dhana, X. Liu, N. T. Aggarwal, D. A. Evans, and K. B. Rajan, “Healthy lifestyle and life expectancy with and without Alzheimer’s dementia: population based cohort study,” BMJ (Clinical research ed.), vol. 377, no. e068390, 2022.
Y. Rao, B. Ganaraja, B. Murlimanju, T. Joy, A. Krishnamurthy, and A. Agrawal, “Hippocampus and its involvement in Alzheimer’s disease: a review,” 3 Biotech, vol. 12, no. 55, pp. 1–10, 2022.
B. Dubois, C. von Arnim, N. Burnie, S. Bozeat, and J. Cummings, “Biomarkers in Alzheimer’s disease: role in early and differential diagnosis and recognition of atypical variants,” Alzheimer’s Research & Therapy, vol. 15, no. 175, pp. 1–13, 2023.
X. Feng, F. Provenzano, and S. Small, “A deep learning MRI approach outperforms other biomarkers of prodromal Alzheimer’s disease,” Alzheimer’s Research & Therapy, vol. 14, no. 45, pp. 1–11, 2022.
T. Klyucherev, P. Olszewski, A. Shalimova, V. Chubarev, V. Tarasov, M. Attwood, S. Syvänen, and H. Schiöth, “Advances in the development of new biomarkers for Alzheimer’s disease,” Translational Neurodegeneration, vol. 11, no. 25, pp. 1–24, 2022.
J. Wu, K. Zhao, Z. Li, D. Wang, Y. Ding, Y. Wei, H. Zhang, and Y. Liu, “A systematic analysis of diagnostic performance for Alzheimer’s disease using structural MRI,” Psychoradiology, vol. 2, no. 1, pp. 1–9, 2022.
G. Karas, P. Scheltens, S. Rombouts, R. van Schijndel, M. Klein, B. Jones, W. van der Flier, H. Vrenken, and F. Barkhof, “Precuneus atrophy in early-onset Alzheimer’s disease: a morphometric structural MRI study,” Neuroradiology, vol. 49, no. 12, pp. 967–976, 2007.
E. Koedman, M. Lehmann, W. van der Flier, P. Scheltens, Y. Pijnenburg, N. Fox, F. Barkhof, and M. Wattjes, “Visual assessment of posterior atrophy development of a MRI rating scale,” European Radiology, vol. 21, pp. 2618–2625, 2011.
L.-O. Wahlund, P. Julin, J. Lindqvist, and P. Scheltens, “Visual assessment of medial temporal lobe atrophy in demented and healthy control subjects: correlation with volumetry,” Psychiatry Research: Neuroimaging, vol. 90, no. 3, pp. 193–199, 1999. [Online]. Available: [link]
J. E. Iglesias, C. 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.
J. Manjón, P. Coupé, L. Martí-Bonmatí, D. Collins, and M. Robles, “Adaptive non-local means denoising of MR images with spatially varying noise levels,” Journal of Magnetic Resonance Imaging, vol. 31, no. 1, pp. 192–203, 2010.
J. Manjón, J. Carbonell-Caballero, J. Lull, L. García-Martí, G. Martí-Bonmatí, and M. Robles, “MRI denoising using Non-Local Means,” Medical Image Analysis, vol. 12, pp. 514–523, 2008.
N. J. Tustison, B. B. Avants, P. A. Cook, Y. Zheng, A. Egan, P. A. Yushkevich, and J. C. Gee, “N4ITK: improved N3 bias correction,” IEEE Transactions on Medical Imaging, vol. 29, no. 6, pp. 1310–1320, 2010.
A. Manera, M. Dadar, V. Fonov, and D. Collins, “CerebrA, registration and manual label correction of Mindboggle-101 atlas for MNI-ICBM152 template,” Scientific Data, vol. 7, no. 1, pp. 1–9, 2020.
B. Avants, N. Tustison, J. Wu, P. Cook, and J. Gee, “An Open Source Multivariate Framework for n-Tissue Segmentation with Evaluation on Public Data,” Neuroinformatics, vol. 9, no. 4, pp. 381–400, 2011.
M. Rebsamen, C. Rummel, M. Reyes, R. Wiest, and R. Mckinley, “Direct cortical thickness estimation using deep learning-based anatomy segmentation and cortex parcellation,” Human Brain Mapping, vol. 41, pp. 4804–4814, 2021.
M. Mendez, “Early-Onset Alzheimer’s Disease,” Neurologic Clinics, vol. 35, no. 2, pp. 263–281, 2017.
L. Apostolova, P. Aisen, A. Eloyan, A. Fagan, K. Fargo, T. Tatiana Foroud, C. Gatsonis, L. Grinberg, C. Jack Jr., J. Kramer, R. Koeppe, W. Kukull, M. Murray, K. Nudelman, M. Rumbaugh, A. Toga, P. Vemuri, A. Trullinger, L. Iaccarino, G. Day, N. Graff-Radford, L. Honig, D. Jones, J. Masdeu, M. Mendez, E. Musiek, C. Onyike, E. Rogalski, S. Salloway, D. Wolk, T. Wingo, M. Carrillo, B. Dickerson, G. Rabinovici, and the LEADS Consortium, “The Longitudinal Early-onset Alzheimer’s Disease Study (LEADS): Framework and methodology,” Alzheimer’s & Dementia, vol. 17, pp. 2043–2055, 2021.
Publicado
30/09/2024
Como Citar
OLIVEIRA, Yasmin V.; FERRARI, Ricardo J..
Automating the Koedam Parietal Atrophy Scale for Alzheimer's Using MRI Features and Clustering Techniques. In: WORKSHOP DE TRABALHOS EM ANDAMENTO - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 37. , 2024, Manaus/AM.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 88-93.
DOI: https://doi.org/10.5753/sibgrapi.est.2024.31650.
