*Result*: Unsupervised Machine Learning for Vascular Mesh Compression.
Title:
Unsupervised Machine Learning for Vascular Mesh Compression.
Authors:
Sehli M; Mines Saint-Etienne, Univ Jean Monnet, INSERM, Saint-Etienne, France., Llona AG; Mines Saint-Etienne, Univ Jean Monnet, INSERM, Saint-Etienne, France., Cotte F; Predisurge, Grande Usine Creative 2, Saint-Etienne, France., Perrin D; Predisurge, Grande Usine Creative 2, Saint-Etienne, France., Avril S; Mines Saint-Etienne, Univ Jean Monnet, INSERM, Saint-Etienne, France.
Source:
International journal for numerical methods in biomedical engineering [Int J Numer Method Biomed Eng] 2025 Dec; Vol. 41 (12), pp. e70124.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Wiley Country of Publication: England NLM ID: 101530293 Publication Model: Print Cited Medium: Internet ISSN: 2040-7947 (Electronic) Linking ISSN: 20407939 NLM ISO Abbreviation: Int J Numer Method Biomed Eng Subsets: MEDLINE
Imprint Name(s):
Original Publication: [Oxford, UK] : Wiley
MeSH Terms:
References:
N. Sakalihasan, R. Limet, and O. D. Defawe, “Abdominal Aortic Aneurysm,” Lancet 365, no. 9470 (2005): 1577–1589.
C. B. Ernst, “Abdominal Aortic Aneurysm,” New England Journal of Medicine 328, no. 16 (1993): 1167–1172.
N. Sakalihasan, J. B. Michel, A. Katsargyris, et al., “Abdominal Aortic Aneurysms,” Nature Reviews Disease Primers 4, no. 1 (2018): 34.
N. Kontopodis, S. Lioudaki, D. Pantidis, G. Papadopoulos, E. Georgakarakos, and C. V. Ioannou, “Advances in Determining Abdominal Aortic Aneurysm Size and Growth,” World Journal of Radiology 8, no. 2 (2016): 148–158.
J. Golledge, “Abdominal Aortic Aneurysm: Update on Pathogenesis and Medical Treatments,” Nature Reviews Cardiology 16, no. 4 (2019): 225–242.
D. B. Buck, J. A. Van Herwaarden, M. L. Sn, et al., “Endovascular Treatment of Abdominal Aortic Aneurysms,” Nature Reviews Cardiology 11, no. 2 (2014): 112–123.
C. Reeps, M. Gee, A. Maier, M. Gurdan, H. H. Eckstein, and W. A. Wall, “The Impact of Model Assumptions on Results of Computational Mechanics in Abdominal Aortic Aneurysm,” Journal of Vascular Surgery 51, no. 3 (2010): 679–688.
C. M. Scotti, J. Jimenez, S. C. Muluk, and E. A. Finol, “Wall Stress and Flow Dynamics in Abdominal Aortic Aneurysms: Finite Element Analysis vs. Fluid–Structure Interaction,” Computer Methods in Biomechanics and Biomedical Engineering 11, no. 3 (2008): 301–322.
F. Lorandon, S. Rinckenbach, N. Settembre, E. Steinmetz, L. S. Du Mont, and S. Avril, “Stress Analysis in AAA Does Not Predict Rupture Location Correctly in Patients With Intraluminal Thrombus,” Annals of Vascular Surgery 79 (2022): 279–289.
M. Gonzalez‐Urquijo, R. G. de Zamacona, A. K. M. Mendoza, et al., “3D Modeling of Blood Flow in Simulated Abdominal Aortic Aneurysm,” Vascular and Endovascular Surgery 55, no. 7 (2021): 677–683.
T. Canchi, S. D. Kumar, E. Y. K. Ng, and S. Narayanan, “A Review of Computational Methods to Predict the Risk of Rupture of Abdominal Aortic Aneurysms,” BioMed Research International 2015, no. 1 (2015): 861627.
S. Celi and S. Berti, Biomechanics and FE Modelling of Aneurysm: Review and Advances in Computational Models (INTECH Open Access Publisher, 2012), 3–26.
S. Avril, M. W. Gee, A. Hemmler, and S. Rugonyi, “Patient‐Specific Computational Modeling of Endovascular Aneurysm Repair: State of the Art and Future Directions,” International Journal for Numerical Methods in Biomedical Engineering 37, no. 12 (2021): e3529.
L. Derycke, S. Avril, and A. Millon, “Patient‐Specific Numerical Simulations of Endovascular Procedures in Complex Aortic Pathologies: Review and Clinical Perspectives,” Journal of Clinical Medicine 12, no. 3 (2023): 766.
D. Daye and T. G. Walker, “Complications of Endovascular Aneurysm Repair of the Thoracic and Abdominal Aorta: Evaluation and Management,” Cardiovascular Diagnosis and Therapy 8, no. 1 (2018): S138–S156.
D. W. Jones, S. E. Deery, D. B. Schneider, et al., “Differences in Patient Selection and Outcomes Based on Abdominal Aortic Aneurysm Diameter Thresholds in the Vascular Quality Initiative,” Journal of Vascular Surgery 70, no. 5 (2019): 1446–1455.
Q. Zheng, S. Saponara, X. Tian, Z. Yu, A. Elhanashi, and R. Yu, “A Real‐Time Constellation Image Classification Method of Wireless Communication Signals Based on the Lightweight Network MobileViT,” Cognitive Neurodynamics 18 (2024): 659–671.
Q. Zheng, X. Tian, Z. Yu, et al., “MobileRaT: A Lightweight Radio Transformer Method for Automatic Modulation Classification in Drone Communication Systems,” Drones 7, no. 10 (2023): 596.
D. Fleischmann, A. S. Chin, L. Molvin, J. Wang, and R. Hallett, “Computed Tomography Angiography: A Review and Technical Update,” Radiologic Clinics 54, no. 1 (2016): 1–12.
V. P. Grover, J. M. Tognarelli, M. M. Crossey, I. J. Cox, S. D. Taylor‐Robinson, and M. J. McPhail, “Magnetic Resonance Imaging: Principles and Techniques: Lessons for Clinicians,” Journal of Clinical and Experimental Hepatology 5, no. 3 (2015): 246–255.
V. Chan and A. Perlas, “Basics of Ultrasound Imaging,” in Atlas of Ultrasound‐Guided Procedures in Interventional Pain Management, ed. S. Narouze (Springer New York, 2011), 13–19.
M. I. Bracco, A. A. Karkhaneh Yousefi, L. Rouet, and S. Avril, “Ultrasound Probe Pressure Affects Aortic Wall Stiffness: A Patient‐Specific Computational Study in Abdominal Aortic Aneurysms,” Annals of Biomedical Engineering 53, no. 1 (2024): 71–82.
P. Lambin, R. T. Leijenaar, T. M. Deist, et al., “Radiomics: The Bridge Between Medical Imaging and Personalized Medicine,” Nature Reviews Clinical Oncology 14, no. 12 (2017): 749–762.
M. Auer and T. C. Gasser, “Reconstruction and Finite Element Mesh Generation of Abdominal Aortic Aneurysms From Computerized Tomography Angiography Data With Minimal User Interactions,” IEEE Transactions on Medical Imaging 29, no. 4 (2010): 1022–1028.
A. Pepe, J. Li, M. Rolf‐Pissarczyk, et al., “Detection, Segmentation, Simulation and Visualization of Aortic Dissections: A Review,” Medical Image Analysis 65 (2020): 101773.
P. Erhart, C. Grond‐Ginsbach, M. Hakimi, et al., “Finite Element Analysis of Abdominal Aortic Aneurysms: Predicted Rupture Risk Correlates With Aortic Wall Histology in Individual Patients,” Journal of Endovascular Therapy 21, no. 4 (2014): 556–564.
S. Lucci, S. M. Musa, and D. Kopec, Artificial Intelligence in the 21st Century (De Gruyter, 2022).
A. Väänänen, K. Haataja, K. Vehviläinen‐Julkunen, and P. Toivanen, “AI in Healthcare: A Narrative Review,” F1000Research 10 (2021): 6.
A. Arzani, J. X. Wang, M. S. Sacks, and S. C. Shadden, “Machine Learning for Cardiovascular Biomechanics Modeling: Challenges and Beyond,” Annals of Biomedical Engineering 50, no. 6 (2022): 615–627.
J. Holmes, L. Sacchi, and R. Bellazzi, “Artificial Intelligence in Medicine,” Annals of the Royal College of Surgeons of England 86 (2004): 334–338.
G. S. Collins and K. G. Moons, “Reporting of Artificial Intelligence Prediction Models,” Lancet 393, no. 10181 (2019): 1577–1579.
A. Winkler‐Schwartz, V. Bissonnette, N. Mirchi, et al., “Artificial Intelligence in Medical Education: Best Practices Using Machine Learning to Assess Surgical Expertise in Virtual Reality Simulation,” Journal of Surgical Education 76, no. 6 (2019): 1681–1690.
J. J. Park, J. Tiefenbach, and A. K. Demetriades, “The Role of Artificial Intelligence in Surgical Simulation,” Frontiers in Medical Technology 4 (2022): 1076755.
Y. Kumar, A. Koul, R. Singla, and M. F. Ijaz, “Artificial Intelligence in Disease Diagnosis: A Systematic Literature Review, Synthesizing Framework and Future Research Agenda,” Journal of Ambient Intelligence and Humanized Computing 14, no. 7 (2023): 8459–8486.
L. Liang, W. Mao, and W. Sun, “A Feasibility Study of Deep Learning for Predicting Hemodynamics of Human Thoracic Aorta,” Journal of Biomechanics 99 (2020): 109544.
T. Lyu, G. Yang, X. Zhao, et al., “Dissected Aorta Segmentation Using Convolutional Neural Networks,” Computer Methods and Programs in Biomedicine 211 (2021): 106417.
J. Zhao, J. Zhao, S. Pang, and Q. Feng, “Segmentation of the True Lumen of Aorta Dissection via Morphology‐Constrained Stepwise Deep Mesh Regression,” IEEE Transactions on Medical Imaging 41, no. 7 (2022): 1826–1836.
E. Pajaziti, J. Montalt‐Tordera, C. Capelli, et al., “Shape‐Driven Deep Neural Networks for Fast Acquisition of Aortic 3D Pressure and Velocity Flow Fields,” PLoS Computational Biology 19, no. 4 (2023): e1011055.
E. Ferdian, D. J. Dubowitz, C. A. Mauger, A. Wang, and A. A. Young, “WSSNet: Aortic Wall Shear Stress Estimation Using Deep Learning on 4D Flow MRI,” Frontiers in Cardiovascular Medicine 8 (2022): 769927.
R. Schnabel and R. Klein, “Octree‐Based Point‐Cloud Compression,” in Eurographics Symposium on Point‐Based Graphics, eds. M. Botsch and B. Chen (The Eurographics Association, 2006), 111–121.
S. Wold, K. Esbensen, and P. Geladi, “Principal Component Analysis,” Chemometrics and Intelligent Laboratory Systems 2, no. 1–3 (1987): 37–52.
C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep Learning on Point Sets for 3D Classification and Segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (Computer Vision Foundation, 2017), 652–660.
K. You, P. Gao, and Q. Li, “IPDAE: Improved Patch‐Based Deep Autoencoder for Lossy Point Cloud Geometry Compression,” in Proceedings of the 1st International Workshop on Advances in Point Cloud Compression, Processing and Analysis (Association for Computing Machinery, 2022), 1–10.
Z. Que, G. Lu, and D. Xu, “Voxelcontext‐Net: An Octree Based Framework for Point Cloud Compression,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (Computer Vision Foundation, 2021), 6042–6051.
T. Y. Du, “Dimensionality Reduction Techniques for Visualizing Morphometric Data: Comparing Principal Component Analysis to Nonlinear Methods,” Evolutionary Biology 46 (2019): 106–121.
K. Bartecki, “PCA‐Based Approximation of a Class of Distributed Parameter Systems: Classical vs. Neural Network Approach,” Bulletin of the Polish Academy of Sciences. Technical Sciences 60, no. 3 (2012): 651–660.
F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini, “The Graph Neural Network Model,” IEEE Transactions on Neural Networks 20, no. 1 (2009): 61–80.
R. J. Cameron, “General Coupling Matrix Synthesis Methods for Chebyshev Filtering Functions,” IEEE Transactions on Microwave Theory and Techniques 47, no. 4 (1999): 433–442.
L. Derycke, S. Avril, J. Vermunt, et al., “Computational Prediction of Proximal Sealing in Endovascular Abdominal Aortic Aneurysm Repair With Unfavorable Necks,” Computer Methods and Programs in Biomedicine 244 (2024): 107993.
C. B. Ernst, “Abdominal Aortic Aneurysm,” New England Journal of Medicine 328, no. 16 (1993): 1167–1172.
N. Sakalihasan, J. B. Michel, A. Katsargyris, et al., “Abdominal Aortic Aneurysms,” Nature Reviews Disease Primers 4, no. 1 (2018): 34.
N. Kontopodis, S. Lioudaki, D. Pantidis, G. Papadopoulos, E. Georgakarakos, and C. V. Ioannou, “Advances in Determining Abdominal Aortic Aneurysm Size and Growth,” World Journal of Radiology 8, no. 2 (2016): 148–158.
J. Golledge, “Abdominal Aortic Aneurysm: Update on Pathogenesis and Medical Treatments,” Nature Reviews Cardiology 16, no. 4 (2019): 225–242.
D. B. Buck, J. A. Van Herwaarden, M. L. Sn, et al., “Endovascular Treatment of Abdominal Aortic Aneurysms,” Nature Reviews Cardiology 11, no. 2 (2014): 112–123.
C. Reeps, M. Gee, A. Maier, M. Gurdan, H. H. Eckstein, and W. A. Wall, “The Impact of Model Assumptions on Results of Computational Mechanics in Abdominal Aortic Aneurysm,” Journal of Vascular Surgery 51, no. 3 (2010): 679–688.
C. M. Scotti, J. Jimenez, S. C. Muluk, and E. A. Finol, “Wall Stress and Flow Dynamics in Abdominal Aortic Aneurysms: Finite Element Analysis vs. Fluid–Structure Interaction,” Computer Methods in Biomechanics and Biomedical Engineering 11, no. 3 (2008): 301–322.
F. Lorandon, S. Rinckenbach, N. Settembre, E. Steinmetz, L. S. Du Mont, and S. Avril, “Stress Analysis in AAA Does Not Predict Rupture Location Correctly in Patients With Intraluminal Thrombus,” Annals of Vascular Surgery 79 (2022): 279–289.
M. Gonzalez‐Urquijo, R. G. de Zamacona, A. K. M. Mendoza, et al., “3D Modeling of Blood Flow in Simulated Abdominal Aortic Aneurysm,” Vascular and Endovascular Surgery 55, no. 7 (2021): 677–683.
T. Canchi, S. D. Kumar, E. Y. K. Ng, and S. Narayanan, “A Review of Computational Methods to Predict the Risk of Rupture of Abdominal Aortic Aneurysms,” BioMed Research International 2015, no. 1 (2015): 861627.
S. Celi and S. Berti, Biomechanics and FE Modelling of Aneurysm: Review and Advances in Computational Models (INTECH Open Access Publisher, 2012), 3–26.
S. Avril, M. W. Gee, A. Hemmler, and S. Rugonyi, “Patient‐Specific Computational Modeling of Endovascular Aneurysm Repair: State of the Art and Future Directions,” International Journal for Numerical Methods in Biomedical Engineering 37, no. 12 (2021): e3529.
L. Derycke, S. Avril, and A. Millon, “Patient‐Specific Numerical Simulations of Endovascular Procedures in Complex Aortic Pathologies: Review and Clinical Perspectives,” Journal of Clinical Medicine 12, no. 3 (2023): 766.
D. Daye and T. G. Walker, “Complications of Endovascular Aneurysm Repair of the Thoracic and Abdominal Aorta: Evaluation and Management,” Cardiovascular Diagnosis and Therapy 8, no. 1 (2018): S138–S156.
D. W. Jones, S. E. Deery, D. B. Schneider, et al., “Differences in Patient Selection and Outcomes Based on Abdominal Aortic Aneurysm Diameter Thresholds in the Vascular Quality Initiative,” Journal of Vascular Surgery 70, no. 5 (2019): 1446–1455.
Q. Zheng, S. Saponara, X. Tian, Z. Yu, A. Elhanashi, and R. Yu, “A Real‐Time Constellation Image Classification Method of Wireless Communication Signals Based on the Lightweight Network MobileViT,” Cognitive Neurodynamics 18 (2024): 659–671.
Q. Zheng, X. Tian, Z. Yu, et al., “MobileRaT: A Lightweight Radio Transformer Method for Automatic Modulation Classification in Drone Communication Systems,” Drones 7, no. 10 (2023): 596.
D. Fleischmann, A. S. Chin, L. Molvin, J. Wang, and R. Hallett, “Computed Tomography Angiography: A Review and Technical Update,” Radiologic Clinics 54, no. 1 (2016): 1–12.
V. P. Grover, J. M. Tognarelli, M. M. Crossey, I. J. Cox, S. D. Taylor‐Robinson, and M. J. McPhail, “Magnetic Resonance Imaging: Principles and Techniques: Lessons for Clinicians,” Journal of Clinical and Experimental Hepatology 5, no. 3 (2015): 246–255.
V. Chan and A. Perlas, “Basics of Ultrasound Imaging,” in Atlas of Ultrasound‐Guided Procedures in Interventional Pain Management, ed. S. Narouze (Springer New York, 2011), 13–19.
M. I. Bracco, A. A. Karkhaneh Yousefi, L. Rouet, and S. Avril, “Ultrasound Probe Pressure Affects Aortic Wall Stiffness: A Patient‐Specific Computational Study in Abdominal Aortic Aneurysms,” Annals of Biomedical Engineering 53, no. 1 (2024): 71–82.
P. Lambin, R. T. Leijenaar, T. M. Deist, et al., “Radiomics: The Bridge Between Medical Imaging and Personalized Medicine,” Nature Reviews Clinical Oncology 14, no. 12 (2017): 749–762.
M. Auer and T. C. Gasser, “Reconstruction and Finite Element Mesh Generation of Abdominal Aortic Aneurysms From Computerized Tomography Angiography Data With Minimal User Interactions,” IEEE Transactions on Medical Imaging 29, no. 4 (2010): 1022–1028.
A. Pepe, J. Li, M. Rolf‐Pissarczyk, et al., “Detection, Segmentation, Simulation and Visualization of Aortic Dissections: A Review,” Medical Image Analysis 65 (2020): 101773.
P. Erhart, C. Grond‐Ginsbach, M. Hakimi, et al., “Finite Element Analysis of Abdominal Aortic Aneurysms: Predicted Rupture Risk Correlates With Aortic Wall Histology in Individual Patients,” Journal of Endovascular Therapy 21, no. 4 (2014): 556–564.
S. Lucci, S. M. Musa, and D. Kopec, Artificial Intelligence in the 21st Century (De Gruyter, 2022).
A. Väänänen, K. Haataja, K. Vehviläinen‐Julkunen, and P. Toivanen, “AI in Healthcare: A Narrative Review,” F1000Research 10 (2021): 6.
A. Arzani, J. X. Wang, M. S. Sacks, and S. C. Shadden, “Machine Learning for Cardiovascular Biomechanics Modeling: Challenges and Beyond,” Annals of Biomedical Engineering 50, no. 6 (2022): 615–627.
J. Holmes, L. Sacchi, and R. Bellazzi, “Artificial Intelligence in Medicine,” Annals of the Royal College of Surgeons of England 86 (2004): 334–338.
G. S. Collins and K. G. Moons, “Reporting of Artificial Intelligence Prediction Models,” Lancet 393, no. 10181 (2019): 1577–1579.
A. Winkler‐Schwartz, V. Bissonnette, N. Mirchi, et al., “Artificial Intelligence in Medical Education: Best Practices Using Machine Learning to Assess Surgical Expertise in Virtual Reality Simulation,” Journal of Surgical Education 76, no. 6 (2019): 1681–1690.
J. J. Park, J. Tiefenbach, and A. K. Demetriades, “The Role of Artificial Intelligence in Surgical Simulation,” Frontiers in Medical Technology 4 (2022): 1076755.
Y. Kumar, A. Koul, R. Singla, and M. F. Ijaz, “Artificial Intelligence in Disease Diagnosis: A Systematic Literature Review, Synthesizing Framework and Future Research Agenda,” Journal of Ambient Intelligence and Humanized Computing 14, no. 7 (2023): 8459–8486.
L. Liang, W. Mao, and W. Sun, “A Feasibility Study of Deep Learning for Predicting Hemodynamics of Human Thoracic Aorta,” Journal of Biomechanics 99 (2020): 109544.
T. Lyu, G. Yang, X. Zhao, et al., “Dissected Aorta Segmentation Using Convolutional Neural Networks,” Computer Methods and Programs in Biomedicine 211 (2021): 106417.
J. Zhao, J. Zhao, S. Pang, and Q. Feng, “Segmentation of the True Lumen of Aorta Dissection via Morphology‐Constrained Stepwise Deep Mesh Regression,” IEEE Transactions on Medical Imaging 41, no. 7 (2022): 1826–1836.
E. Pajaziti, J. Montalt‐Tordera, C. Capelli, et al., “Shape‐Driven Deep Neural Networks for Fast Acquisition of Aortic 3D Pressure and Velocity Flow Fields,” PLoS Computational Biology 19, no. 4 (2023): e1011055.
E. Ferdian, D. J. Dubowitz, C. A. Mauger, A. Wang, and A. A. Young, “WSSNet: Aortic Wall Shear Stress Estimation Using Deep Learning on 4D Flow MRI,” Frontiers in Cardiovascular Medicine 8 (2022): 769927.
R. Schnabel and R. Klein, “Octree‐Based Point‐Cloud Compression,” in Eurographics Symposium on Point‐Based Graphics, eds. M. Botsch and B. Chen (The Eurographics Association, 2006), 111–121.
S. Wold, K. Esbensen, and P. Geladi, “Principal Component Analysis,” Chemometrics and Intelligent Laboratory Systems 2, no. 1–3 (1987): 37–52.
C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep Learning on Point Sets for 3D Classification and Segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (Computer Vision Foundation, 2017), 652–660.
K. You, P. Gao, and Q. Li, “IPDAE: Improved Patch‐Based Deep Autoencoder for Lossy Point Cloud Geometry Compression,” in Proceedings of the 1st International Workshop on Advances in Point Cloud Compression, Processing and Analysis (Association for Computing Machinery, 2022), 1–10.
Z. Que, G. Lu, and D. Xu, “Voxelcontext‐Net: An Octree Based Framework for Point Cloud Compression,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (Computer Vision Foundation, 2021), 6042–6051.
T. Y. Du, “Dimensionality Reduction Techniques for Visualizing Morphometric Data: Comparing Principal Component Analysis to Nonlinear Methods,” Evolutionary Biology 46 (2019): 106–121.
K. Bartecki, “PCA‐Based Approximation of a Class of Distributed Parameter Systems: Classical vs. Neural Network Approach,” Bulletin of the Polish Academy of Sciences. Technical Sciences 60, no. 3 (2012): 651–660.
F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini, “The Graph Neural Network Model,” IEEE Transactions on Neural Networks 20, no. 1 (2009): 61–80.
R. J. Cameron, “General Coupling Matrix Synthesis Methods for Chebyshev Filtering Functions,” IEEE Transactions on Microwave Theory and Techniques 47, no. 4 (1999): 433–442.
L. Derycke, S. Avril, J. Vermunt, et al., “Computational Prediction of Proximal Sealing in Endovascular Abdominal Aortic Aneurysm Repair With Unfavorable Necks,” Computer Methods and Programs in Biomedicine 244 (2024): 107993.
Grant Information:
Saint-Etienne Metropole
Contributed Indexing:
Keywords: abdominal aortic aneurysm; convolutional neural network; graph neural network; principal component analysis; unsupervised learning
Entry Date(s):
Date Created: 20251208 Date Completed: 20251208 Latest Revision: 20251208
Update Code:
20260130
DOI:
10.1002/cnm.70124
PMID:
41360499
Database:
MEDLINE
*Further Information*
*Machine learning (ML) models are becoming increasingly valuable for cardiovascular prediction and simulation, offering critical support for medical decision-making. These models are particularly useful for predicting disease progression and evaluating potential treatments. A major challenge in these models is to preserve the geometric fidelity of meshes while optimizing parameter efficiency to reduce memory usage, computational resources and execution time. In this paper, we present innovative approaches to abdominal aortic aneurysm (AAA) mesh compression, utilizing both statistical and deep learning models, with a focus on unsupervised learning techniques. We explore principal component analysis (PCA) as a statistical method and compare it with several deep learning models, including a simple autoencoder, an enhanced autoencoder based on PCA, a convolutional neural network (CNN), and a graph neural network (GNN). Human aortas are compressed using different statistical and deep learning methods to get the most relevant features. The mesh is reconstructed using the computed features and the error of the reconstructed meshes is compared. Our results indicate that PCA, using 64 principal components, outperforms deep learning models with a comparable latent space of 64, achieving the best overall performance. Among the deep learning approaches, the PCA-based autoencoder demonstrates the highest effectiveness.
(© 2025 John Wiley & Sons Ltd.)*