An End-to-End Deep Learning Generative Framework for Refinable Shape Matching and Generation

Generative modelling for shapes is a prerequisite for In-Silico Clinical Trials (ISCTs), which aim to cost-effectively validate medical device interventions using synthetic anatomical shapes, often represented as 3D surface meshes. However, constructing AI models to generate shapes closely resembling the real mesh samples is challenging due to variable vertex counts, connectivities, and the lack of dense vertex-wise correspondences across the training data. Employing graph representations for meshes, we develop a novel unsupervised geometric deep-learning model to establish refinable shape correspondences in a latent space, construct a population-derived atlas and generate realistic synthetic shapes. We additionally extend our proposed base model to a joint shape generative-clustering multi-atlas framework to incorporate further variability and preserve more details in the generated shapes. Experimental results using liver and left-ventricular models demonstrate the approach's applicability to computational medicine, highlighting its suitability for ISCTs through a comparative analysis

An End-to-End Deep Learning Generative Framework for Refinable Shape Matching and Generation

Generative modelling for shapes is a prerequisite for In-Silico Clinical Trials (ISCTs), which aim to cost-effectively validate medical device interventions using synthetic anatomical shapes, often represented as 3D surface meshes. However, constructing AI models to generate shapes closely resembling the real mesh samples is challenging due to variable vertex counts, connectivities, and the lack of dense vertex-wise correspondences across the training data. Employing graph representations for meshes, we develop a novel unsupervised geometric deep-learning model to establish refinable shape correspondences in a latent space, construct a population-derived atlas and generate realistic synthetic shapes. We additionally extend our proposed base model to a joint shape generative-clustering multi-atlas framework to incorporate further variability and preserve more details in the generated shapes. Experimental results using liver and left-ventricular models demonstrate the approach's applicability to computational medicine, highlighting its suitability for ISCTs through a comparative analysis

Graph-based probabilistic geometric deep learning framework for generation of virtual anatomical populations

In the field of medical imaging, "shape generation" refers to the computational techniques employed to create accurate and detailed representations of anatomical structures/organs. Shape generation plays a crucial role in medical imaging, profoundly impacting clinical applications and diagnostics. Accurate representation of anatomical structures is essential for disease detection, treatment planning, personalized medicine, and computational modelling. Leveraging machine learning and computational modelling opens avenues for valuable insights through In-Silico Clinical Trials (ISCTs). In ISCTs, virtual populations of anatomical shapes are vital for evaluating clinical devices. These populations must capture anatomical and physiological variability while remaining plausible to ensure meaningful and reliable results. By generating virtual shape populations, researchers can simulate and assess medical interventions, accelerating the development of improved therapies and devices. However, constructing generative models faces challenges due to the fact that real-world anatomical shapes, derived from different subjects, exhibit varying topological structures and, in general, there is no topological correspondence among shapes from different subjects. This thesis aims to address the challenges associated with shape matching and generation by introducing an unsupervised probabilistic deep generative model, applicable to datasets including shape surface mesh data with varying topological structures and the absence of correspondences. The proposed framework leverages graph representations to capture the geometric characteristics of anatomical shapes and incorporates advanced techniques in geometric deep learning. By employing these algorithms, the framework is able to establish a learnable set of vertex-wise correspondences between shapes in the latent space while learning/constructing a population-derived atlas model. Subsequently, the model generates virtual populations of anatomical shapes that closely resemble real-world data. This novel generative framework is designed to handle variable mesh topology across patients/input shapes and successfully synthesises anatomically plausible virtual populations with significant variability in shape and diverse topologies. These capabilities expand the potential applications of the approach in computational medicine and make it well-suited for ISCTs

A Geometric Deep Learning Framework for Generation of Virtual Left Ventricles as Graphs

Generative statistical models have a wide range of applications in the modelling of anatomies. In-silico clinical trials of medical devices, for instance, require the development of virtual populations of anatomy that capture enough variability while remaining plausible. Model construction and use are heavily influenced by the correspondence problem and establishing shape matching over a large number of training data. This study focuses on generating virtual cohorts of left ventricle geometries resembling different-sized shape populations, suitable for in-silico experiments. We present an unsupervised data-driven probabilistic generative model for shapes. This framework incorporates an attention-based shape matching procedure using graph neural networks, coupled with a β−VAE generation model, eliminating the need for initial shape correspondence. Left ventricle shapes derived from cardiac magnetic resonance images available in the UK Biobank are utilized for training and validating the framework. We investigate our method's generative capabilities in terms of generalisation and specificity and show that it is able to synthesise virtual populations of realistic shapes with volumetric measurements in line with actual clinical indices. Moreover, results show our method outperforms joint registration-PCA-based models.</p