MyoMesh: Um Pipeline 3D Open-Source para Geração de Modelos Cardíacos Personalizados
Resumo
Este trabalho descreve um pipeline computacional tridimensional open-source em Python para geração de modelos cardíacos personalizados voltados à simulação eletrofisiológica. A partir de segmentações em ressonância magnética cardíaca, o método reconstrói a geometria biventricular, gera uma malha volumétrica tetraédrica com fronteiras anatômicas rotuladas e incorpora fibrose miocárdica em core e gray zone por marcação volumétrica hierárquica. Além disso, estima a orientação de fibras por elemento por uma abordagem rule-based baseada em campos elípticos. O resultado é um modelo completo, com rótulos teciduais e microestrutura, pronto para uso em simulações eletrofisiológicas.
Referências
Alnaes, M. S., Blechta, J., Hake, J., Johansson, A., Kehlet, B., Logg, A., Richardson, C., Ring, J., Rognes, M. E., and Wells, G. N. (2015). The fenics project version 1.5. Archive of Numerical Software, 3.
Banerjee, A., Camps, J., Zacur, E., Andrews, C. M., Rudy, Y., Choudhury, R. P., Rodriguez, B., and Grau, V. (2021). A completely automated pipeline for 3d reconstruction of human heart from 2d cine magnetic resonance slices. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 379(2212):20200257.
Bayer, J. D., Blake, R. C., Plank, G., and Trayanova, N. A. (2012). A novel rule-based algorithm for assigning myocardial fiber orientation to computational heart models. Annals of Biomedical Engineering, 40:2243–2254.
Camps, J., Berg, L. A., Wang, Z. J., et al. (2024). Digital twinning of the human ventricular activation sequence to clinical 12-lead ECGs and magnetic resonance imaging using realistic purkinje networks for in silico clinical trials. Medical Image Analysis, 94:103108.
Corral-Acero, J. et al. (2020). The “Digital Twin” to enable the vision of precision cardiology. European Heart Journal, 41(48):4556–4564.
Doste, R., Camps, J., Wang, Z. J., Berg, L. A., Holmes, M., Smith, H., Beetz, M., Li, L., Banerjee, A., Grau, V., and Rodriguez, B. (2026). An automated computational pipeline for generating large-scale cohorts of patient-specific ventricular models in electromechanical in silico trials. Computer Methods and Programs in Biomedicine, page 109290.
Heiberg, E., Sjögren, J., Ugander, M., Carlsson, M., Engblom, H., and Arheden, H. (2010). Design and validation of segment–freely available software for cardiovascular image analysis. BMC Medical Imaging, 10(1):1–13.
Huynh, K. (2016). Arrhythmia risk stratification using virtual heart models. Nature Reviews Cardiology, 13(7):381–381.
Neic, A., Gsell, M. A., Karabelas, E., Prassl, A. J., and Plank, G. (2020). Automating image-based mesh generation and manipulation tasks in cardiac modeling workflows using meshtool. SoftwareX, 11:100454.
openCARP Consortium et al. (2025). openCARP.
Razeghi, O., Solís-Lemus, J. A., Lee, A. W., Karim, R., Corrado, C., Roney, C. H., de Vecchi, A., and Niederer, S. A. (2020). Cemrgapp: An interactive medical imaging application with image processing, computer vision, and machine learning toolkits for cardiovascular research. SoftwareX, 12:100570.
Sachetto, R., Alonso, S., and dos Santos, R. W. (2018). Killing many birds with two stones: Hypoxia and fibrosis can generate ectopic beats in a human ventricular model. Frontiers in Physiology, 9.
Sachetto Oliveira, R., Martins Rocha, B., Burgarelli, D., Meira Jr, W., Constantinides, C., and Weber dos Santos, R. (2018). Performance evaluation of GPU parallelization, space-time adaptive algorithms, and their combination for simulating cardiac electrophysiology. International Journal for Numerical Methods in Biomedical Engineering, 34(2):e2913.
Schroeder, W., Martin, K., and Lorensen, B. (2006). The Visualization Toolkit (4th ed.). Kitware.
Scott, A. D., Wylezinska, M., and Birch, M. J. (2015). Motion artefacts in mri: a complex problem with many partial solutions. Journal of Cardiovascular Magnetic Resonance, 17(1):1–12.
Strøm, V. (2018). Using personalized virtual hearts to assess arrhythmia risk in acute infarction patients. Master’s thesis, University of Oslo.
Banerjee, A., Camps, J., Zacur, E., Andrews, C. M., Rudy, Y., Choudhury, R. P., Rodriguez, B., and Grau, V. (2021). A completely automated pipeline for 3d reconstruction of human heart from 2d cine magnetic resonance slices. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 379(2212):20200257.
Bayer, J. D., Blake, R. C., Plank, G., and Trayanova, N. A. (2012). A novel rule-based algorithm for assigning myocardial fiber orientation to computational heart models. Annals of Biomedical Engineering, 40:2243–2254.
Camps, J., Berg, L. A., Wang, Z. J., et al. (2024). Digital twinning of the human ventricular activation sequence to clinical 12-lead ECGs and magnetic resonance imaging using realistic purkinje networks for in silico clinical trials. Medical Image Analysis, 94:103108.
Corral-Acero, J. et al. (2020). The “Digital Twin” to enable the vision of precision cardiology. European Heart Journal, 41(48):4556–4564.
Doste, R., Camps, J., Wang, Z. J., Berg, L. A., Holmes, M., Smith, H., Beetz, M., Li, L., Banerjee, A., Grau, V., and Rodriguez, B. (2026). An automated computational pipeline for generating large-scale cohorts of patient-specific ventricular models in electromechanical in silico trials. Computer Methods and Programs in Biomedicine, page 109290.
Heiberg, E., Sjögren, J., Ugander, M., Carlsson, M., Engblom, H., and Arheden, H. (2010). Design and validation of segment–freely available software for cardiovascular image analysis. BMC Medical Imaging, 10(1):1–13.
Huynh, K. (2016). Arrhythmia risk stratification using virtual heart models. Nature Reviews Cardiology, 13(7):381–381.
Neic, A., Gsell, M. A., Karabelas, E., Prassl, A. J., and Plank, G. (2020). Automating image-based mesh generation and manipulation tasks in cardiac modeling workflows using meshtool. SoftwareX, 11:100454.
openCARP Consortium et al. (2025). openCARP.
Razeghi, O., Solís-Lemus, J. A., Lee, A. W., Karim, R., Corrado, C., Roney, C. H., de Vecchi, A., and Niederer, S. A. (2020). Cemrgapp: An interactive medical imaging application with image processing, computer vision, and machine learning toolkits for cardiovascular research. SoftwareX, 12:100570.
Sachetto, R., Alonso, S., and dos Santos, R. W. (2018). Killing many birds with two stones: Hypoxia and fibrosis can generate ectopic beats in a human ventricular model. Frontiers in Physiology, 9.
Sachetto Oliveira, R., Martins Rocha, B., Burgarelli, D., Meira Jr, W., Constantinides, C., and Weber dos Santos, R. (2018). Performance evaluation of GPU parallelization, space-time adaptive algorithms, and their combination for simulating cardiac electrophysiology. International Journal for Numerical Methods in Biomedical Engineering, 34(2):e2913.
Schroeder, W., Martin, K., and Lorensen, B. (2006). The Visualization Toolkit (4th ed.). Kitware.
Scott, A. D., Wylezinska, M., and Birch, M. J. (2015). Motion artefacts in mri: a complex problem with many partial solutions. Journal of Cardiovascular Magnetic Resonance, 17(1):1–12.
Strøm, V. (2018). Using personalized virtual hearts to assess arrhythmia risk in acute infarction patients. Master’s thesis, University of Oslo.
Publicado
01/06/2026
Como Citar
NAMORATO, Filipe de Lima; SANTOS, Rodrigo Weber dos; CAMPOS, Joventino de Oliveira.
MyoMesh: Um Pipeline 3D Open-Source para Geração de Modelos Cardíacos Personalizados. In: CONCURSO DE TRABALHOS DE INICIAÇÃO CIENTÍFICA - SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 26. , 2026, Ouro Preto/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 30-35.
ISSN 2763-8987.
DOI: https://doi.org/10.5753/sbcas_estendido.2026.21532.
