A novel deep learning-driven approach for predicting the pelvis soft-tissue deformations toward a real-time interactive childbirth simulation

Adler, 2010, Quantifying colocalization by correlation: the Pearson correlation coefficient is superior to the Mander's overlap coefficient, Cytometry, 77A, 733, 10.1002/cyto.a.20896 Akiba, 2019 Armon, 2011, Geometry and mechanics in the opening of chiral seed pods, Science, 333, 1726, 10.1126/science.1203874 Ballit, 2022, HyperMSM: a new MSM variant for efficient simulation of dynamic soft-tissue deformations, Comput. Methods Progr. Biomed., 216, 10.1016/j.cmpb.2022.106659 Ballit, 2022, Recurrent neural network to predict hyperelastic constitutive behaviors of the skeletal muscle, Med. Biol. Eng. Comput., 60, 10.1007/s11517-022-02541-z Ballit, 2023, Fast soft-tissue deformations coupled with mixed reality toward the next-generation childbirth training simulator, Med. Biol. Eng. Comput., 61, 2207, 10.1007/s11517-023-02864-5 Benesty, 2008, On the importance of the Pearson correlation coefficient in noise reduction, IEEE Trans. Audio Speech Lang. Process., 16, 757, 10.1109/TASL.2008.919072 Borotikar, 2017, Dynamic MRI to quantify musculoskeletal motion: a systematic review of concurrent validity and reliability, and perspectives for evaluation of musculoskeletal disorders, PLoS One, 12, 10.1371/journal.pone.0189587 Bouaziz, 2023, Projective dynamics: fusing constraint projections for fast simulation, vol. 2 Brandt, 2018, Hyper-reduced projective dynamics, ACM Trans. Graph., 37, 10.1145/3197517.3201387 Brion, 2021, Domain adversarial networks and intensity-based data augmentation for male pelvic organ segmentation in cone beam CT, Comput. Biol. Med., 131, 10.1016/j.compbiomed.2021.104269 Butler, 2018, Machine learning for molecular and materials science, Nature, 559, 547, 10.1038/s41586-018-0337-2 Buttin, 2013, Biomechanical simulation of the fetal descent without imposed theoretical trajectory, Comput. Methods Progr. Biomed., 111, 389, 10.1016/j.cmpb.2013.04.005 Chen, 2020, Childbirth computational models: characteristics and applications, J. Biomech. Eng., 143 Chen, 1996, Young's modulus measurements of soft tissues with application to elasticity imaging, IEEE Trans. Ultrason. Ferroelectrics Freq. Control, 43, 191, 10.1109/58.484478 Chen, 2022, A computational procedure to derive the curve of Carus for childbirth computational modeling, J. Biomech. Eng., 145 Dandıl, 2021, Detection of pseudo brain tumors via stacked LSTM neural networks using MR spectroscopy signals, Biocybern. Biomed. Eng., 41, 173, 10.1016/j.bbe.2020.12.003 Dao, 2019, From deep learning to transfer learning for the prediction of skeletal muscle forces, Med. Biol. Eng. Comput., 57, 1049, 10.1007/s11517-018-1940-y Dao, 2018, A systematic review of continuum modeling of skeletal muscles: current trends, limitations, and recommendations, Appl. Bionics Biomech., 2018, 10.1155/2018/7631818 Dosovitskiy, 2020 Fujita, 2020, AI-based computer-aided diagnosis (AI-CAD): the latest review to read first, Radiol. Phys. Technol., 13, 6, 10.1007/s12194-019-00552-4 Funahashi, 1989, On the approximate realization of continuous mappings by neural networks, Neural Network., 2, 183, 10.1016/0893-6080(89)90003-8 Fung, 1981 Garnier, 2021, A review on deep reinforcement learning for fluid mechanics, Comput. Fluids, 225, 10.1016/j.compfluid.2021.104973 Gers, 1999, Learning to forget: continual prediction with LSTM, vol. 2, 850 Graves, 2005, Framewise phoneme classification with bidirectional LSTM and other neural network architectures, Neural Network., 18, 602, 10.1016/j.neunet.2005.06.042 Grimm, 2021, Forces involved with labor and delivery—a biomechanical perspective, Ann. Biomed. Eng., 49, 1819, 10.1007/s10439-020-02718-3 Haghighat, 2021, A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics, Comput. Methods Appl. Mech. Eng., 379, 10.1016/j.cma.2021.113741 He, 2019, Pelvic organ segmentation using distinctive curve guided fully convolutional networks, IEEE Trans. Med. Imag., 38, 585, 10.1109/TMI.2018.2867837 He, 2021, Manifold learning based data-driven modeling for soft biological tissues, J. Biomech., 117, 10.1016/j.jbiomech.2020.110124 Hochreiter, 1997, Long short-term memory, Neural Comput., 9, 1735, 10.1162/neco.1997.9.8.1735 Holden, 2019, Subspace neural physics: fast data-driven interactive simulation Hornik, 1989, Multilayer feedforward networks are universal approximators, Neural Network., 2, 359, 10.1016/0893-6080(89)90020-8 Joldes, 2019, Suite of meshless algorithms for accurate computation of soft tissue deformation for surgical simulation, Med. Image Anal., 56, 152, 10.1016/j.media.2019.06.004 Khadem, 2021, Nucleation and growth of cholesteric collagen tactoids: a time-series statistical analysis based on integration of direct numerical simulation (DNS) and long short-term memory recurrent neural network (LSTM-RNN), J. Colloid Interface Sci., 582, 859, 10.1016/j.jcis.2020.08.052 Kirchdoerfer, 2016, Data-driven computational mechanics, Comput. Methods Appl. Mech. Eng., 304, 81, 10.1016/j.cma.2016.02.001 Kirchdoerfer, 2018, Data-driven computing in dynamics, Int. J. Numer. Methods Eng., 113, 1697, 10.1002/nme.5716 Kollmann, 2020, Deep learning for topology optimization of 2D metamaterials, Mater. Des., 196, 10.1016/j.matdes.2020.109098 Lepage, 2015, Biomechanical pregnant pelvic system model and numerical simulation of childbirth: impact of delivery on the uterosacral ligaments, preliminary results, Int Urogynecol J, 26, 497, 10.1007/s00192-014-2498-3 Liang, 2018, A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite-element analysis, J. R. Soc. Interface, 15, 10.1098/rsif.2017.0844 Liu, 2019, Bidirectional LSTM with attention mechanism and convolutional layer for text classification, Neurocomputing, 337, 325, 10.1016/j.neucom.2019.01.078 Ma, 2009, A review of algorithms for medical image segmentation and their applications to the female pelvic cavity, Comput. Methods Biomech. Biomed. Eng., 13, 235, 10.1080/10255840903131878 Madani, 2019, Bridging finite element and machine learning modeling: stress prediction of arterial walls in atherosclerosis, J. Biomech. Eng., 141, 10.1115/1.4043290 Maziar, 2020, Hidden fluid mechanics: learning velocity and pressure fields from flow visualizations, Science, 367, 1026, 10.1126/science.aaw4741 Meister, 2020, Deep learning acceleration of Total Lagrangian Explicit Dynamics for soft tissue mechanics, Comput. Methods Appl. Mech. Eng., 358, 10.1016/j.cma.2019.112628 Mendizabal, 2020, Physics-based deep neural network for real-time lesion tracking in ultrasound-guided breast biopsy, 33 Mou, 2021, Data-driven variational multiscale reduced order models, Comput. Methods Appl. Mech. Eng., 373, 10.1016/j.cma.2020.113470 Nguyen, 2019, Forecasting damage mechanics by deep learning, Comput. Mater. Continua (CMC), 61, 951, 10.32604/cmc.2019.08001 Nguyen, 2020, A systematic review of real-time medical simulations with soft-tissue deformation: computational approaches, interaction devices, system architectures, and clinical validations, Appl. Bionics Biomech., 2020, 10.1155/2020/5039329 Nguyen-Le, 2020, A data-driven approach based on long short-term memory and hidden Markov model for crack propagation prediction, Eng. Fract. Mech., 235, 10.1016/j.engfracmech.2020.107085 Obrezkov, 2020, A finite element for soft tissue deformation based on the absolute nodal coordinate formulation, Acta Mech., 231, 1519, 10.1007/s00707-019-02607-4 Ooi, 2020, Modeling transient fluid simulations with proper orthogonal decomposition and machine learning, Int. J. Numer. Methods Fluid., 93, 396, 10.1002/fld.4888 Pellicer-Valero, 2020, Real-time biomechanical modeling of the liver using Machine Learning models trained on Finite Element Method simulations, Expert Syst. Appl., 143, 10.1016/j.eswa.2019.113083 Pfeiffer, 2019, Learning soft tissue behavior of organs for surgical navigation with convolutional neural networks, Int. J. Comput. Assist. Radiol. Surg., 14, 1147, 10.1007/s11548-019-01965-7 Phellan Aro, 2020, Real‐time biomechanics using the finite element method and machine learning: review and perspective, Med. Phys., 48 Rumelhart, 1986, Learning representations by back-propagating errors, Nature, 323, 533, 10.1038/323533a0 Sack, 2023, Magnetic resonance elastography from fundamental soft-tissue mechanics to diagnostic imaging, Nature Reviews Physics, 5, 25, 10.1038/s42254-022-00543-2 Santesteban, 2020, SoftSMPL: data-driven modeling of nonlinear soft-tissue dynamics for parametric humans, Comput. Graph. Forum, 39, 65, 10.1111/cgf.13912 Schütt, 2018, SchNet – a deep learning architecture for molecules and materials, J. Chem. Phys., 148, 10.1063/1.5019779 Song, 2022, Reduced-order extended kalman filter for deformable tissue simulation, J. Mech. Phys. Solid., 158, 10.1016/j.jmps.2021.104696 Song, 2023, Maximum likelihood-based extended Kalman filter for soft tissue modelling, J. Mech. Behav. Biomed. Mater., 137, 10.1016/j.jmbbm.2022.105553 Srivastava, 2014, Dropout: a simple way to prevent neural networks from overfitting, J. Mach. Learn. Res., 15, 1929 Tac, 2022, Data-driven modeling of the mechanical behavior of anisotropic soft biological tissue, Eng. Comput., 38, 4167, 10.1007/s00366-022-01733-3 Tonutti, 2017, A machine learning approach for real-time modelling of tissue deformation in image-guided neurosurgery, Artif. Intell. Med., 80, 39, 10.1016/j.artmed.2017.07.004 Treuille, 2006, Model reduction for real-time fluids, ACM Trans. Graph., 25, 826, 10.1145/1141911.1141962 Vaswani, 2017, Attention is all you need Vila Pouca, 2018, Viscous effects in pelvic floor muscles during childbirth: a numerical study, Int J Numer Method Biomed Eng, 34, 10.1002/cnm.2927 Ward, 2005, Density and hydration of fresh and fixed human skeletal muscle, J. Biomech., 38, 2317, 10.1016/j.jbiomech.2004.10.001 Xie, 2022, Constrained finite element method for runtime modeling of soft tissue deformation, Appl. Math. Model., 109, 599, 10.1016/j.apm.2022.05.020 Zaroug, 2020, Lower limb kinematics trajectory prediction using long short-term memory neural networks, Front. Bioeng. Biotechnol., 8, 10.3389/fbioe.2020.00362 Zhang, 2019, Neural network modelling of soft tissue deformation for surgical simulation, Artif. Intell. Med., 97, 61, 10.1016/j.artmed.2018.11.001 Zhong, 2006, A Cellular Neural Network Methodology for Deformable Object Simulation, IEEE Trans. Inf. Technol. Biomed., 10, 749, 10.1109/TITB.2006.875679