Rationale
Mathematical modelling and computer simulation have proven extraordinarily successful in classical engineering disciplines, where governing physical laws, well-defined material properties, and controlled boundary conditions enable accurate prediction and optimisation of system behaviour. One of the most significant and enduring challenges for the computational mechanics community is extending this success beyond traditional engineering domains into medicine, biomedical sciences, and biology. In these domains, systems are characterised by complex geometries, heterogeneous and evolving material properties, strong coupling across spatial and temporal scales, and limited or noisy observational data. Computational biomechanics provides a principled and unifying framework for addressing these challenges by combining physical laws, biological constraints, and patient-specific information derived from medical imaging and clinical data.
The MICCAI Workshop on Computational Biomechanics for Medicine XXI continues a long-standing and well-established workshop series that serves as a dedicated forum for researchers working at the intersection of biomechanics, medical image computing, and computer-assisted intervention. The workshop brings together specialists in computational biomechanics with the broader MICCAI community to present recent methodological advances, share emerging applications, and exchange perspectives on future directions in computer-integrated medicine. Its scope aligns directly with core MICCAI themes, including medical image analysis, image-guided surgery, surgical simulation and planning, computer-assisted and robotic interventions, and the integration of physics-based and data-driven approaches for clinical decision support.
Biomechanical modelling occupies a unique position within MICCAI by offering mechanistic insight and physically grounded constraints that complement statistical and machine-learning-based methods. Continuum mechanics models can provide a rational basis for analysing biomedical images by constraining estimated deformations, motions, and physiological processes to biologically plausible behaviours. Such constraints can improve robustness, interpretability, and generalisability of image analysis pipelines, particularly in settings with limited annotated data. Beyond image analysis, biomechanical models enable quantitative interpretation of imaging data by linking observed anatomy and motion to underlying physical properties such as tissue stiffness, stress, strain, and fluid flow, which are often directly relevant to clinical decision-making.
A key strength of computational biomechanics lies in its ability to integrate information across multiple spatial and temporal scales, ranging from molecular and cellular processes to tissue, organ, and whole-body behaviour. Multiscale and multiphysics models allow the synthesis of heterogeneous data sources into coherent, predictive representations of biological systems. These models can provide clinically significant insights into disease mechanisms, progression, and treatment response, supporting applications such as risk stratification, prognosis, surgical planning, and outcome prediction. In the context of computer-assisted intervention, biomechanics-based simulations play a central role in surgical rehearsal, intraoperative guidance, implant and device design, and evaluation of novel surgical techniques.
The increasing prominence of artificial intelligence within MICCAI further amplifies the relevance of computational biomechanics. Physics-based models and simulations offer powerful tools for generating realistic synthetic data to train, validate, and stress-test machine-learning systems, particularly in scenarios where clinical data are scarce, biased, or ethically difficult to obtain. Such approaches can improve robustness, fairness, and transparency of AI systems for medical imaging and intervention. Hybrid methods that couple data-driven learning with biomechanical constraints or differentiable physics models represent an emerging research direction with strong potential for advancing digital twins, virtual patients, and personalised medicine.
The primary goal of the MICCAI Workshop on Computational Biomechanics for Medicine XXI is to showcase the scientific and clinical utility of computational biomechanics within computer-integrated medicine, while fostering critical discussion on methodological challenges, translational barriers, and emerging opportunities.
Previous workshops in the series.
Code of Conduct: CBM XXI Workshop follows the MICCAI Society Code of Conduct.
This Code of Conduct applies to all CBM XXI Workshop participants.
Keynote Speakers:
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Programme:
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