55 research outputs found
A Conditional Flow Variational Autoencoder for Controllable Synthesis of Virtual Populations of Anatomy
The generation of virtual populations (VPs) of anatomy is essential for
conducting in silico trials of medical devices. Typically, the generated VP
should capture sufficient variability while remaining plausible and should
reflect the specific characteristics and demographics of the patients observed
in real populations. In several applications, it is desirable to synthesise
virtual populations in a \textit{controlled} manner, where relevant covariates
are used to conditionally synthesise virtual populations that fit a specific
target population/characteristics. We propose to equip a conditional
variational autoencoder (cVAE) with normalising flows to boost the flexibility
and complexity of the approximate posterior learnt, leading to enhanced
flexibility for controllable synthesis of VPs of anatomical structures. We
demonstrate the performance of our conditional flow VAE using a data set of
cardiac left ventricles acquired from 2360 patients, with associated
demographic information and clinical measurements (used as
covariates/conditional information). The results obtained indicate the
superiority of the proposed method for conditional synthesis of virtual
populations of cardiac left ventricles relative to a cVAE. Conditional
synthesis performance was evaluated in terms of generalisation and specificity
errors and in terms of the ability to preserve clinically relevant biomarkers
in synthesised VPs, that is, the left ventricular blood pool and myocardial
volume, relative to the real observed population.Comment: Accepted at MICCAI 202
A Generative Shape Compositional Framework: Towards Representative Populations of Virtual Heart Chimaeras
Generating virtual populations of anatomy that capture sufficient variability
while remaining plausible is essential for conducting in-silico trials of
medical devices. However, not all anatomical shapes of interest are always
available for each individual in a population. Hence,
missing/partially-overlapping anatomical information is often available across
individuals in a population. We introduce a generative shape model for complex
anatomical structures, learnable from datasets of unpaired datasets. The
proposed generative model can synthesise complete whole complex shape
assemblies coined virtual chimaeras, as opposed to natural human chimaeras. We
applied this framework to build virtual chimaeras from databases of whole-heart
shape assemblies that each contribute samples for heart substructures.
Specifically, we propose a generative shape compositional framework which
comprises two components - a part-aware generative shape model which captures
the variability in shape observed for each structure of interest in the
training population; and a spatial composition network which assembles/composes
the structures synthesised by the former into multi-part shape assemblies (viz.
virtual chimaeras). We also propose a novel self supervised learning scheme
that enables the spatial composition network to be trained with partially
overlapping data and weak labels. We trained and validated our approach using
shapes of cardiac structures derived from cardiac magnetic resonance images
available in the UK Biobank. Our approach significantly outperforms a PCA-based
shape model (trained with complete data) in terms of generalisability and
specificity. This demonstrates the superiority of the proposed approach as the
synthesised cardiac virtual populations are more plausible and capture a
greater degree of variability in shape than those generated by the PCA-based
shape model.Comment: 15 pages, 4 figure
A Generative Shape Compositional Framework to Synthesise Populations of Virtual Chimaeras
Generating virtual populations of anatomy that capture sufficient variability while remaining plausible is essential for conducting in-silico trials of medical devices. However, not all anatomical shapes of interest are always available for each individual in a population. Hence, missing/partially-overlapping anatomical information is often available across individuals in a population. We introduce a generative shape model for complex anatomical structures, learnable from datasets of unpaired datasets. The proposed generative model can synthesise complete whole complex shape assemblies coined virtual chimaeras, as opposed to natural human chimaeras. We applied this framework to build virtual chimaeras from databases of whole-heart shape assemblies that each contribute samples for heart substructures. Specifically, we propose a generative shape compositional framework which comprises two components - a part-aware generative shape model which captures the variability in shape observed for each structure of interest in the training population; and a spatial composition network which assembles/composes the structures synthesised by the former into multi-part shape assemblies (viz. virtual chimaeras). We also propose a novel self supervised learning scheme that enables the spatial composition network to be trained with partially overlapping data and weak labels. We trained and validated our approach using shapes of cardiac structures derived from cardiac magnetic resonance images available in the UK Biobank. Our approach significantly outperforms a PCA-based shape model (trained with complete data) in terms of generalisability and specificity. This demonstrates the superiority of the proposed approach as the synthesised cardiac virtual populations are more plausible and capture a greater degree of variability in shape than those generated by the PCA-based shape model
Shape-guided Conditional Latent Diffusion Models for Synthesising Brain Vasculature
The Circle of Willis (CoW) is the part of cerebral vasculature responsible
for delivering blood to the brain. Understanding the diverse anatomical
variations and configurations of the CoW is paramount to advance research on
cerebrovascular diseases and refine clinical interventions. However,
comprehensive investigation of less prevalent CoW variations remains
challenging because of the dominance of a few commonly occurring
configurations. We propose a novel generative approach utilising a conditional
latent diffusion model with shape and anatomical guidance to generate realistic
3D CoW segmentations, including different phenotypical variations. Our
conditional latent diffusion model incorporates shape guidance to better
preserve vessel continuity and demonstrates superior performance when compared
to alternative generative models, including conditional variants of 3D GAN and
3D VAE. We observed that our model generated CoW variants that are more
realistic and demonstrate higher visual fidelity than competing approaches with
an FID score 53\% better than the best-performing GAN-based model
Enhancing Building Semantic Segmentation Accuracy with Super Resolution and Deep Learning: Investigating the Impact of Spatial Resolution on Various Datasets
The development of remote sensing and deep learning techniques has enabled
building semantic segmentation with high accuracy and efficiency. Despite their
success in different tasks, the discussions on the impact of spatial resolution
on deep learning based building semantic segmentation are quite inadequate,
which makes choosing a higher cost-effective data source a big challenge. To
address the issue mentioned above, in this study, we create remote sensing
images among three study areas into multiple spatial resolutions by
super-resolution and down-sampling. After that, two representative deep
learning architectures: UNet and FPN, are selected for model training and
testing. The experimental results obtained from three cities with two deep
learning models indicate that the spatial resolution greatly influences
building segmentation results, and with a better cost-effectiveness around
0.3m, which we believe will be an important insight for data selection and
preparation
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