arXiv subjects

Gabriel Bernardino

Publications and source records attributed to Gabriel Bernardino.

8 recordsLinked to original sources

arXiv2026

Uncertainty-Aware Missing-Data Multimodal Latent for Fetal-Growth Analysis

Objective: Routine third-trimester examination yields fetal biometry, maternal, Doppler and fetal-cardiac measurements, acquired at clinical discretion and therefore often incomplete. We show that these four measurement blocks are close to mutually uninformative, and that this single property determines what a representation of them can impute, what it can audit, and what fetal size alone cannot indicate. Methods: A linear-Gaussian factor model (K = 8 by parallel analysis, VARIMAX-rotated) was fitted to 25 measurements in four blocks from 977 fetuses (169 SGA, 61 severe; 77 LGA). Posterior precision sums contributions from observed measurements only, so missing values are marginalized rather than imputed. Data quality was screened using the standardized residual between each measurement and its reconstruction. Results: Predicting any one block from the other three gives an out-of-fold R2 of 0.023. The representation is a continuous growth spectrum with no cluster structure (Hartigan dip p = 0.99, gap statistic k = 1, three-cluster silhouette 0.07) ordering fetuses by birthweight centile (Spearman rho = 0.55). Among 169 SGA fetuses the haemodynamic redistribution axis separated the 25 adverse outcomes (AUC 0.70, 0.585-0.808) where measured size did not (0.60, 0.451-0.738). With Doppler censored, the marginalized interval covered held-out measurements in 97% and 93% of cases against nominal 95% and 90%. The reconstruction residual flagged 38 of 977 records, 36 confirmed transcription errors in the registry. Conclusion: Marginalizing missing measurements yields a representation whose uncertainty reflects the available data, and whose reconstruction residual doubles as a data-quality screen. Because the blocks are nearly independent, confirmed flags are within-block errors, and a synthetic benchmark gives the coupling needed before cross-block detection becomes available.

stat.AP↗

arXiv2026

Visualizing definitional divergence in high-dimensional data by manifold alignment: Application to 3D right ventricular strain computations

Medical imaging studies often rely on a single sample per subject, assuming it is representative of their physiological traits. However, variations in how input descriptors are defined or computed (e.g. due to a lack of consensus in the scientific field) may have a crucial impact on the analysis, and are hardly considered in practice. In this paper, we propose an original strategy based on representation learning to estimate a parametric map reflecting the impact of such definitional differences on a given physiological descriptor, previously extracted from medical images. We consider the different definitions or computations of such physiological descriptors as different high-dimensional data, potentially of heterogeneous types. We specifically focus on myocardial deformation (strain), for which there is limited agreement on its definition. We first use manifold alignment to match the latent representations associated with the different definitions of this descriptor. Then, we formulate plausible distributions in the latent space to represent definitional divergence across descriptors, from which we reconstruct a high-dimensional parametric map to visualize such definitional divergence. Due to the lack of proper ground truth for this specific clinical application, we first demonstrate this methodology on toy experiments and then expand the evaluation on right ventricular strain data from subjects obtained from 3D echocardiographic image sequences, for which different types of strain are available at each point of the right ventricle endocardial surface mesh. Beyond this illustrative application, our methodology has the potential to be generalised to many other population analyses considering heterogeneous high-dimensional descriptors.

cs.CV↗

arXiv2026

Sampling-guided exploration of active feature selection policies

Determining the most appropriate features for machine learning predictive models is challenging regarding performance and feature acquisition costs. In particular, global feature choice is limited given that some features will only benefit a subset of instances. In previous work, we proposed a reinforcement learning approach to sequentially recommend which modality to acquire next to reach the best information/cost ratio, based on the instance-specific information already acquired. We formulated the problem as a Markov Decision Process where the state's dimensionality changes during the episode, avoiding data imputation, contrary to existing works. However, this only allowed processing a small number of features, as all possible combinations of features were considered. Here, we address these limitations with two contributions: 1) we expand our framework to larger datasets with a heuristic-based strategy that focuses on the most promising feature combinations, and 2) we introduce a post-fit regularisation strategy that reduces the number of different feature combinations, leading to compact sequences of decisions. We tested our method on four binary classification datasets (one involving high-dimensional variables), the largest of which had 56 features and 4500 samples. We obtained better performance than state-of-the-art methods, both in terms of accuracy and policy complexity.

cs.LG↗

arXiv2026

AGE-US: automated gestational age estimation based on fetal ultrasound images

Being born small carries significant health risks, including increased neonatal mortality and a higher likelihood of future cardiac diseases. Accurate estimation of gestational age is critical for monitoring fetal growth, but traditional methods, such as estimation based on the last menstrual period, are in some situations difficult to obtain. While ultrasound-based approaches offer greater reliability, they rely on manual measurements that introduce variability. This study presents an interpretable deep learning-based method for automated gestational age calculation, leveraging a novel segmentation architecture and distance maps to overcome dataset limitations and the scarcity of segmentation masks. Our approach achieves performance comparable to state-of-the-art models while reducing complexity, making it particularly suitable for resource-constrained settings and with limited annotated data. Furthermore, our results demonstrate that the use of distance maps is particularly suitable for estimating femur endpoints.

eess.IV↗

arXiv2021

Volumetric parcellation of the right ventricle for regional geometric and functional assessment

3D echocardiography is an increasingly popular tool for assessing cardiac remodelling in the right ventricle (RV). It allows quantification of the cardiac chambers without any geometric assumptions, which is the main weakness of 2D echocardiography. However, regional quantification of geometry and function is limited by the lower spatial and temporal resolution and the scarcity of identifiable anatomical landmarks. We developed a technique for regionally assessing the 3 relevant RV regions: apical, inlet and outflow. The method's inputs are end-diastolic (ED) and end-systolic (ES) segmented 3D surface models. The method first defines a partition of the ED endocardium using the geodesic distances from each surface point to apex, tricuspid valve and pulmonary valve: the landmarks that define the 3 regions. The ED surface mesh is then tetrahedralised, and the endocardial-defined partition is interpolated in the blood cavity via the Laplace equation. For obtaining an ES partition, the endocardial partition is transported from ED to ES using a commercial image-based tracking, and then interpolated towards the endocardium, similarly to ED, for computing volumes and ejection fraction (EF). We present a full assessment of the method's validity and reproducibility. First, we assess reproducibility under segmentation variability, obtaining intra- and inter- observer errors (4-10% and 10-23% resp.). Finally, we use a synthetic remodelling dataset to identify the situations in which our method is able to correctly determine the region that has remodelled. This dataset is generated by a novel mesh reconstruction method that deforms a reference mesh, locally imposing a given strain, expressed in anatomical coordinates. We show that the parcellation method is adequate for capturing local circumferential and global circumferential and longitudinal RV remodelling.

eess.IV↗

arXiv2020

Handling confounding variables in statistical shape analysis -- application to cardiac remodelling

Statistical shape analysis is a powerful tool to assess organ morphologies and find shape changes associated to a particular disease. However, imbalance in confounding factors, such as demographics might invalidate the analysis if not taken into consideration. Despite the methodological advances in the field, providing new methods that are able to capture complex and regional shape differences, the relationship between non-imaging information and shape variability has been overlooked. We present a linear statistical shape analysis framework that finds shape differences unassociated to a controlled set of confounding variables. It includes two confounding correction methods: confounding deflation and adjustment. We applied our framework to a cardiac magnetic resonance imaging dataset, consisting of the cardiac ventricles of 89 triathletes and 77 controls, to identify cardiac remodelling due to the practice of endurance exercise. To test robustness to confounders, subsets of this dataset were generated by randomly removing controls with low body mass index, thus introducing imbalance. The analysis of the whole dataset indicates an increase of ventricular volumes and myocardial mass in athletes, which is consistent with the clinical literature. However, when confounders are not taken into consideration no increase of myocardial mass is found. Using the downsampled datasets, we find that confounder adjustment methods are needed to find the real remodelling patterns in imbalanced datasets.

cs.CV↗

arXiv2020

Fast quasi-conformal regional flattening of the left atrium

Two-dimensional representation of 3D anatomical structures is a simple and intuitive way for analysing patient information across populations and image modalities. It also allows convenient visualizations that can be included in clinical reports for a fast overview of the whole structure. While cardiac ventricles, especially the left ventricle, have an established standard representation (e.g. bull's eye plot), the 2D depiction of the left atrium (LA) is challenging due to its sub-structural complexity including the pulmonary veins (PV) and the left atrial appendage (LAA). Quasi-conformal flattening techniques, successfully applied to cardiac ventricles, require additional constraints in the case of the LA to place the PV and LAA in the same geometrical 2D location for different cases. Some registration-based methods have been proposed but 3D (or 2D) surface registration is time-consuming and prone to errors if the geometries are very different. We propose a novel atrial flattening methodology where a quasi-conformal 2D map of the LA is obtained quickly and without errors related to registration. In our approach, the LA is divided into 5 regions which are then mapped to their analogue two-dimensional regions. A dataset of 67 human left atria from magnetic resonance images (MRI) was studied to derive a population-based 2D LA template representing the averaged relative locations of the PVs and LAA. The clinical application of the proposed methodology is illustrated on different use cases including the integration of MRI and electroanatomical data.

cs.GR↗

arXiv2018

A rule-based method to model myocardial fiber orientation in cardiac biventricular geometries with outflow tracts

Rule-based methods are often used for assigning fiber orientation to cardiac anatomical models. However, existing methods have been developed using data mostly from the left ventricle. As a consequence, fiber information obtained from rule-based methods often does not match histological data in other areas of the heart such as the right ventricle, having a negative impact in cardiac simulations beyond the left ventricle. In this work, we present a rule-based method where fiber orientation is separately modeled in each ventricle following observations from histology. This allows to create detailed fiber orientation in specific regions such as the endocardium of the right ventricle, the interventricular septum and the outflow tracts. We also carried out electrophysiological simulations involving these structures and with different fiber configurations. In particular, we built a modelling pipeline for creating patient-specific volumetric meshes of biventricular geometries, including the outflow tracts, and subsequently simulate the electrical wavefront propagation in outflow tract ventricular arrhythmias with different origins for the ectopic focus. The resulting simulations with the proposed rule-based method showed a very good agreement with clinical parameters such as the 10 ms isochrone ratio in a cohort of nine patients suffering from this type of arrhythmia. The developed modelling pipeline confirms its potential for an in silico identification of the site of origin in outflow tract ventricular arrhythmias before clinical intervention.

physics.med-ph↗