GHOST-CAT: An Efficient and Practical Network for Mesh Generation from 3D Echocardiography

Recent advances in deep learning have significantly accelerated cardiac imaging workflows, from segmentation to the generation of meshes for computational modelling. Nevertheless, analysis of 3D echocardiograms presents unique challenges due to their low contrast-to-noise ratio, conical field of view, and susceptibility to acoustic shadowing. Here, we present an efficient and practical network tailored for 3D echocardiograms. Our method consists of a two-stage network that combines convolutional neural networks, graph convolutional networks, and transformers, to create accurate time-varying 3D meshes of the left ventricle that are topologically consistent and temporally coherent throughout the cardiac cycle. Our model achieved superior mesh reconstruction accuracy compared to current state-of-the-art methods on a held-out test dataset of 100 3D echo images, with a Dice coefficient of 0.87 +/- 0.05 (cavity) and 0.75 +/- 0.07 (myocardium), and mean +/- SD surface distances of 3.3 +/- 0.6 mm (endocardium) and 3.5 +/- 0.5 mm (epicardium), against reference segmentations derived from cardiac magnetic resonance imaging. The reconstructed mesh enables automated calculation of routine clinical indices, such as volume, mass, and strain, and enables advanced applications with biophysical digital twins. Source code is openly shared at https://github.com/EdwardFerdian/ghost-cat.

Diffeomorphic Multi-Resolution Deep Learning Registration for Applications in Breast MRI

In breast surgical planning, accurate registration of MR images across patient positions has the potential to improve the localisation of tumours during breast cancer treatment. While learning-based registration methods have recently become the state-of-the-art approach for most medical image registration tasks, these methods have yet to make inroads into breast image registration due to certain difficulties-the lack of rich texture information in breast MR images and the need for the deformations to be diffeomophic. In this work, we propose learning strategies for breast MR image registration that are amenable to diffeomorphic constraints, together with early experimental results from in-silico and in-vivo experiments. One key contribution of this work is a registration network which produces superior registration outcomes for breast images in addition to providing diffeomorphic guarantees.