139 research outputs found
Medical image segmentation and analysis using statistical shape modelling and inter-landmark relationships
The study of anatomical morphology is of great importance to medical imaging, with applications varying from clinical diagnosis to computer-aided surgery. To this end, automated tools are required for accurate extraction of the anatomical boundaries from the image data and detailed interpretation of morphological information. This thesis introduces a novel approach to shape-based analysis of medical images based on Inter- Landmark Descriptors (ILDs). Unlike point coordinates that describe absolute position, these shape variables represent relative configuration of landmarks in the shape. The proposed work is motivated by the inherent difficulties of methods based on landmark coordinates in challenging applications. Through explicit invariance to pose parameters and decomposition of the global shape constraints, this work permits anatomical shape analysis that is resistant to image inhomogeneities and geometrical inconsistencies. Several algorithms are presented to tackle specific image segmentation and analysis problems, including automatic initialisation, optimal feature point search, outlier handling and dynamic abnormality localisation. Detailed validation results are provided based on various cardiovascular magnetic resonance datasets, showing increased robustness and accuracy.Open acces
Construction of a Statistical Atlas of the Whole Heart from a Large 4D CT Database
International audienceWe present in this work an efficient and robust framework for the construction of a high-resolution and spatio-temporal atlas of the whole heart from a database of 138 CT 4D images, the largest sample to be used for cardiac statistical modeling to date. The data is drawn from a variety of pathologies, which benefits its generalization to new subjects and physiological studies. In the proposed technique, spatial and temporal normalization based on non-rigid image registration are used to synthesize a population mean image from all CT image. With the resulting transformation, a detailed 3D mesh representation of the atlas is warped to fit all images in each subject and phase. The obtained level of anatomical detail (a total of 13 cardiac structures) and the extendability of the atlas present an advantage over most existing cardiac models published previously
USLR: an open-source tool for unbiased and smooth longitudinal registration of brain MR
We present USLR, a computational framework for longitudinal registration of
brain MRI scans to estimate nonlinear image trajectories that are smooth across
time, unbiased to any timepoint, and robust to imaging artefacts. It operates
on the Lie algebra parameterisation of spatial transforms (which is compatible
with rigid transforms and stationary velocity fields for nonlinear deformation)
and takes advantage of log-domain properties to solve the problem using
Bayesian inference. USRL estimates rigid and nonlinear registrations that: (i)
bring all timepoints to an unbiased subject-specific space; and (i) compute a
smooth trajectory across the imaging time-series. We capitalise on
learning-based registration algorithms and closed-form expressions for fast
inference. A use-case Alzheimer's disease study is used to showcase the
benefits of the pipeline in multiple fronts, such as time-consistent image
segmentation to reduce intra-subject variability, subject-specific prediction
or population analysis using tensor-based morphometry. We demonstrate that such
approach improves upon cross-sectional methods in identifying group
differences, which can be helpful in detecting more subtle atrophy levels or in
reducing sample sizes in clinical trials. The code is publicly available in
https://github.com/acasamitjana/uslrComment: Submitted to Medical Image Analysi
Current and Future Role of Artificial Intelligence in Cardiac Imaging
Cardiovascular disease remains the most common cause of morbidity and mortality worldwide, and thus an important focus for medical research and medical imaging. Despite continuous advances in cardiac imaging modalities, including echocardiography, cardiovascular magnetic resonance and cardiac computed tomography, the heart remains a challenging organ to image, in particular due to its perpetual motion. Other challenges faced by cardiac imaging include respiratory motion, complex geometry of the ventricles and atria, variability in imaging conditions and protocols, oblique orientation of the heart with respect to the body, and the small size of some of the cardiac structures, including the coronary arteries, trabeculae and papillary muscles
Domain generalization in deep learning-based mass detection in mammography: A large-scale multi-center study
Computer-aided detection systems based on deep learning have shown great
potential in breast cancer detection. However, the lack of domain
generalization of artificial neural networks is an important obstacle to their
deployment in changing clinical environments. In this work, we explore the
domain generalization of deep learning methods for mass detection in digital
mammography and analyze in-depth the sources of domain shift in a large-scale
multi-center setting. To this end, we compare the performance of eight
state-of-the-art detection methods, including Transformer-based models, trained
in a single domain and tested in five unseen domains. Moreover, a single-source
mass detection training pipeline is designed to improve the domain
generalization without requiring images from the new domain. The results show
that our workflow generalizes better than state-of-the-art transfer
learning-based approaches in four out of five domains while reducing the domain
shift caused by the different acquisition protocols and scanner manufacturers.
Subsequently, an extensive analysis is performed to identify the covariate
shifts with bigger effects on the detection performance, such as due to
differences in patient age, breast density, mass size, and mass malignancy.
Ultimately, this comprehensive study provides key insights and best practices
for future research on domain generalization in deep learning-based breast
cancer detection
A radiomics approach to analyze cardiac alterations in hypertension
Hypertension is a medical condition that is well-established as a risk factor
for many major diseases. For example, it can cause alterations in the cardiac
structure and function over time that can lead to heart related morbidity and
mortality. However, at the subclinical stage, these changes are subtle and
cannot be easily captured using conventional cardiovascular indices calculated
from clinical cardiac imaging. In this paper, we describe a radiomics approach
for identifying intermediate imaging phenotypes associated with hypertension.
The method combines feature selection and machine learning techniques to
identify the most subtle as well as complex structural and tissue changes in
hypertensive subgroups as compared to healthy individuals. Validation based on
a sample of asymptomatic hearts that include both hypertensive and
non-hypertensive cases demonstrate that the proposed radiomics model is capable
of detecting intensity and textural changes well beyond the capabilities of
conventional imaging phenotypes, indicating its potential for improved
understanding of the longitudinal effects of hypertension on cardiovascular
health and disease
Fairness and bias correction in machine learning for depression prediction across four study populations
A significant level of stigma and inequality exists in mental healthcare, especially in under-served populations. Inequalities are reflected in the data collected for scientific purposes. When not properly accounted for, machine learning (ML) models learned from data can reinforce these structural inequalities or biases. Here, we present a systematic study of bias in ML models designed to predict depression in four different case studies covering different countries and populations. We find that standard ML approaches regularly present biased behaviors. We also show that mitigation techniques, both standard and our own post-hoc method, can be effective in reducing the level of unfair bias. There is no one best ML model for depression prediction that provides equality of outcomes. This emphasizes the importance of analyzing fairness during model selection and transparent reporting about the impact of debiasing interventions. Finally, we also identify positive habits and open challenges that practitioners could follow to enhance fairness in their models.</p
Fairness and bias correction in machine learning for depression prediction across four study populations
A significant level of stigma and inequality exists in mental healthcare, especially in under-served populations. Inequalities are reflected in the data collected for scientific purposes. When not properly accounted for, machine learning (ML) models learned from data can reinforce these structural inequalities or biases. Here, we present a systematic study of bias in ML models designed to predict depression in four different case studies covering different countries and populations. We find that standard ML approaches regularly present biased behaviors. We also show that mitigation techniques, both standard and our own post-hoc method, can be effective in reducing the level of unfair bias. There is no one best ML model for depression prediction that provides equality of outcomes. This emphasizes the importance of analyzing fairness during model selection and transparent reporting about the impact of debiasing interventions. Finally, we also identify positive habits and open challenges that practitioners could follow to enhance fairness in their models.</p
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