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Deep learning for cardiac image segmentation: A review
Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research
Deep learning in medical image registration: introduction and survey
Image registration (IR) is a process that deforms images to align them with
respect to a reference space, making it easier for medical practitioners to
examine various medical images in a standardized reference frame, such as
having the same rotation and scale. This document introduces image registration
using a simple numeric example. It provides a definition of image registration
along with a space-oriented symbolic representation. This review covers various
aspects of image transformations, including affine, deformable, invertible, and
bidirectional transformations, as well as medical image registration algorithms
such as Voxelmorph, Demons, SyN, Iterative Closest Point, and SynthMorph. It
also explores atlas-based registration and multistage image registration
techniques, including coarse-fine and pyramid approaches. Furthermore, this
survey paper discusses medical image registration taxonomies, datasets,
evaluation measures, such as correlation-based metrics, segmentation-based
metrics, processing time, and model size. It also explores applications in
image-guided surgery, motion tracking, and tumor diagnosis. Finally, the
document addresses future research directions, including the further
development of transformers
Construction of boundary element models in bioelectromagnetism
Multisensor electro- and magnetoencephalographic (EEG and MEG) as well as electro- and magnetocardiographic (ECG and MCG) recordings have been proved useful in noninvasively extracting information on bioelectric excitation. The anatomy of the patient needs to be taken into account, when excitation sites are localized by solving the inverse problem. In this work, a methodology has been developed to construct patient specific boundary element models for bioelectromagnetic inverse problems from magnetic resonance (MR) data volumes as well as from two orthogonal X-ray projections. The process consists of three main steps: reconstruction of 3-D geometry, triangulation of reconstructed geometry, and registration of the model with a bioelectromagnetic measurement system. The 3-D geometry is reconstructed from MR data by matching a 3-D deformable boundary element template to images. The deformation is accomplished as an energy minimization process consisting of image and model based terms. The robustness of the matching is improved by multi-resolution and global-to-local approaches as well as using oriented distance maps. A boundary element template is also used when 3-D geometry is reconstructed from X-ray projections. The deformation is first accomplished in 2-D for the contours of simulated, built from the template, and real X-ray projections. The produced 2-D vector field is back-projected and interpolated on the 3-D template surface. A marching cube triangulation is computed for the reconstructed 3-D geometry. Thereafter, a non-iterative mesh-simplification method is applied. The method is based on the Voronoi-Delaunay duality on a 3-D surface with discrete distance measures. Finally, the triangulated surfaces are registered with a bioelectromagnetic measurement utilizing markers. More than fifty boundary element models have been successfully constructed from MR images using the methods developed in this work. A simulation demonstrated the feasibility of X-ray reconstruction; some practical problems of X-ray imaging need to be solved to begin tests with real data.reviewe
High level vision with the deformable pyramid
Une pyramide de graphes, constituant un modèle multirésoltuion déformable est utilisée pour la reconnaissance de formes. Cette pyramide permet de décrire des formes complexes par des maillages de leur surface, elle est construite à partir d'un volume de référence. Le modèle est ensuite déformé pour s'adapter aux données tout en conservant ses propriétés topologiques et géométriques. Cette nouvelle méthode permet l'extraction rapide, robuste et précise du modèle dans le cadre de l'imagerie cardiaque volumique par résoance magnétique. Les potentialités de la pyramide déformable sont illustrées par l'extraction d'un modèle du thorax et du coeur en mouvement
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
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