394 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
Double Diffusion Encoding Prevents Degeneracy in Parameter Estimation of Biophysical Models in Diffusion MRI
Purpose: Biophysical tissue models are increasingly used in the
interpretation of diffusion MRI (dMRI) data, with the potential to provide
specific biomarkers of brain microstructural changes. However, the general
Standard Model has recently shown that model parameter estimation from dMRI
data is ill-posed unless very strong magnetic gradients are used. We analyse
this issue for the Neurite Orientation Dispersion and Density Imaging with
Diffusivity Assessment (NODDIDA) model and demonstrate that its extension from
Single Diffusion Encoding (SDE) to Double Diffusion Encoding (DDE) solves the
ill-posedness and increases the accuracy of the parameter estimation. Methods:
We analyse theoretically the cumulant expansion up to fourth order in b of SDE
and DDE signals. Additionally, we perform in silico experiments to compare SDE
and DDE capabilities under similar noise conditions. Results: We prove
analytically that DDE provides invariant information non-accessible from SDE,
which makes the NODDIDA parameter estimation injective. The in silico
experiments show that DDE reduces the bias and mean square error of the
estimation along the whole feasible region of 5D model parameter space.
Conclusions: DDE adds additional information for estimating the model
parameters, unexplored by SDE, which is enough to solve the degeneracy in the
NODDIDA model parameter estimation.Comment: 22 pages, 7 figure
Multi-stage Biomarker Models for Progression Estimation in Alzheimer’s Disease
The estimation of disease progression in Alzheimer’s disease
(AD) based on a vector of quantitative biomarkers is of high interest
to clinicians, patients, and biomedical researchers alike. In this work,
quantile regression is employed to learn statistical models describing the
evolution of such biomarkers. Two separate models are constructed using
(1) subjects that progress from a cognitively normal (CN) stage to mild
cognitive impairment (MCI) and (2) subjects that progress from MCI
to AD during the observation window of a longitudinal study. These
models are then automatically combined to develop a multi-stage disease
progression model for the whole disease course. A probabilistic approach
is derived to estimate the current disease progress (DP) and the disease
progression rate (DPR) of a given individual by fitting any acquired
biomarkers to these models. A particular strength of this method is that
it is applicable even if individual biomarker measurements are missing
for the subject. Employing cognitive scores and image-based biomarkers,
the presented method is used to estimate DP and DPR for subjects from
the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Further, the
potential use of these values as features for different classification tasks
is demonstrated. For example, accuracy of 64% is reached for CN vs.
MCI vs. AD classification
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
Joint segmentation and discontinuity-preserving deformable registration: Application to cardiac cine-MR images
Medical image registration is a challenging task involving the estimation of
spatial transformations to establish anatomical correspondence between pairs or
groups of images. Recently, deep learning-based image registration methods have
been widely explored, and demonstrated to enable fast and accurate image
registration in a variety of applications. However, most deep learning-based
registration methods assume that the deformation fields are smooth and
continuous everywhere in the image domain, which is not always true, especially
when registering images whose fields of view contain discontinuities at
tissue/organ boundaries. In such scenarios, enforcing smooth, globally
continuous deformation fields leads to incorrect/implausible registration
results. We propose a novel discontinuity-preserving image registration method
to tackle this challenge, which ensures globally discontinuous and locally
smooth deformation fields, leading to more accurate and realistic registration
results. The proposed method leverages the complementary nature of image
segmentation and registration and enables joint segmentation and pair-wise
registration of images. A co-attention block is proposed in the segmentation
component of the network to learn the structural correlations in the input
images, while a discontinuity-preserving registration strategy is employed in
the registration component of the network to ensure plausibility in the
estimated deformation fields at tissue/organ interfaces. We evaluate our method
on the task of intra-subject spatio-temporal image registration using
large-scale cinematic cardiac magnetic resonance image sequences, and
demonstrate that our method achieves significant improvements over the
state-of-the-art for medical image registration, and produces high-quality
segmentation masks for the regions of interest
Modality Completion via Gaussian Process Prior Variational Autoencoders for Multi-Modal Glioma Segmentation
In large studies involving multi protocol Magnetic Resonance Imaging (MRI),
it can occur to miss one or more sub-modalities for a given patient owing to
poor quality (e.g. imaging artifacts), failed acquisitions, or hallway
interrupted imaging examinations. In some cases, certain protocols are
unavailable due to limited scan time or to retrospectively harmonise the
imaging protocols of two independent studies. Missing image modalities pose a
challenge to segmentation frameworks as complementary information contributed
by the missing scans is then lost. In this paper, we propose a novel model,
Multi-modal Gaussian Process Prior Variational Autoencoder (MGP-VAE), to impute
one or more missing sub-modalities for a patient scan. MGP-VAE can leverage the
Gaussian Process (GP) prior on the Variational Autoencoder (VAE) to utilize the
subjects/patients and sub-modalities correlations. Instead of designing one
network for each possible subset of present sub-modalities or using frameworks
to mix feature maps, missing data can be generated from a single model based on
all the available samples. We show the applicability of MGP-VAE on brain tumor
segmentation where either, two, or three of four sub-modalities may be missing.
Our experiments against competitive segmentation baselines with missing
sub-modality on BraTS'19 dataset indicate the effectiveness of the MGP-VAE
model for segmentation tasks.Comment: Accepted in MICCAI 202
Multi-Task Learning Approach for Natural Images' Quality Assessment
Blind image quality assessment (BIQA) is a method to predict the quality of a natural image without the presence of a reference image. Current BIQA models typically learn their prediction separately for different image distortions, ignoring the relationship between the learning tasks. As a result, a BIQA model may has great prediction performance for natural images affected by one particular type of distortion but is less effective when tested on others. In this paper, we propose to address this limitation by training our BIQA model simultaneously under different distortion conditions using multi-task learning (MTL) technique. Given a set of training images, our Multi-Task Learning based Image Quality assessment (MTL-IQ) model first extracts spatial domain BIQA features. The features are then used as an input to a trace-norm regularisation based MTL framework to learn prediction models for different distortion classes simultaneously. For a test image of a known distortion, MTL-IQ selects a specific trained model to predict the image’s quality score. For a test image of an unknown distortion, MTLIQ first estimates the amount of each distortion present in the image using a support vector classifier. The probability estimates are then used to weigh the image prediction scores from different trained models. The weighted scores are then pooled to obtain the final image quality score. Experimental results on standard image quality assessment (IQA) databases show that MTL-IQ is highly correlated with human perceptual measures of image quality. It also obtained higher prediction performance in both overall and individual distortion cases compared to current BIQA models
- …