Fast left ventricle tracking using localized anatomical affine optical flow

Fast left ventricle tracking using localized anatomical affine optical flowIn daily clinical cardiology practice, left ventricle (LV) global and regional function assessment is crucial for disease diagnosis, therapy selection, and patient follow-up. Currently, this is still a time-consuming task, spending valuable human resources. In this work, a novel fast methodology for automatic LV tracking is proposed based on localized anatomically constrained affine optical flow. This novel method can be combined to previously proposed segmentation frameworks or manually delineated surfaces at an initial frame to obtain fully delineated datasets and, thus, assess both global and regional myocardial function. Its feasibility and accuracy were investigated in 3 distinct public databases, namely in realistically simulated 3D ultrasound, clinical 3D echocardiography, and clinical cine cardiac magnetic resonance images. The method showed accurate tracking results in all databases, proving its applicability and accuracy for myocardial function assessment. Moreover, when combined to previous state-of-the-art segmentation frameworks, it outperformed previous tracking strategies in both 3D ultrasound and cardiac magnetic resonance data, automatically computing relevant cardiac indices with smaller biases and narrower limits of agreement compared to reference indices. Simultaneously, the proposed localized tracking method showed to be suitable for online processing, even for 3D motion assessment. Importantly, although here evaluated for LV tracking only, this novel methodology is applicable for tracking of other target structures with minimal adaptations.The authors acknowledge funding support from FCT - Fundacao para a Ciência e a Tecnologia, Portugal, and the European Social Found, European Union, through the Programa Operacional Capital Humano (POCH) in the scope of the PhD grants SFRH/BD/93443/2013 (S. Queiros) and SFRH/BD/95438/2013 (P. Morais), and by the project ’PersonalizedNOS (01-0145-FEDER-000013)’ co-funded by Programa Operacional Regional do Norte (Norte2020) through the European Regional Development Fund (ERDF).info:eu-repo/semantics/publishedVersio

Segmentation de séquences échocardiographiques 2D par ensembles de niveaux contraints par a priori de forme et de mouvement

The aim of this work is to propose an algorithm to segment and track the myocardium using the level-set formalism. The myocardium is first approximated by a geometric model (hyperquadrics) which allows to handle asymetric shapes such as the myocardium while avoiding a learning step. This representation is then embedded into the level-set formalism as a shape prior for the joint segmentation of the endocardial and epicardial borders. This shape prior term is coupled with a local data attachment term and a thickness term that prevents both contours from merging. The algorithm is validated on a dataset of 80 images at end diastolic and end systolic phase with manual references from 3 cardiologists. In a second step, we propose to segment whole sequences using motion information. To this end, we apply a level conservation constraint on the implicit function associated to the level-set and express this contraint as an energy term in a variational framework. This energy is then added to the previously described algorithm in order to constrain the temporal evolution of the contour. Finally the algorithm is validated on 20 echocardiographic sequences with manual references of 2 experts (corresponding to approximately 1200 images).L’objectif de cette thèse est de proposer un algorithme de segmentation et de suivi du myocarde basé sur le formalisme des ensembles de niveaux. Nous modélisons dans un premier temps le myocarde par un modèle géométrique (hyperquadriques) qui permet de représenter des formes asymétriques telles que le myocarde tout en évitant une étape d’apprentissage. Ce modèle est ensuite inclus dans le formalisme des ensembles de niveaux afin de servir de contrainte de forme lors de la segmentation simultanée de l’endocarde et de l’épicarde. Ce terme d’a priori de forme est couplé à un terme local d’attache aux données ainsi qu’à un terme évitant la fusion des deux contours. L’algorithme est validé sur 80 images en fin systole et en fin diastole segmentées par 3 cardiologues. Dans un deuxième temps, nous proposons de segmenter l’ensemble d’une séquence en utilisant l’information de mouvement. Dans ce but, nous faisons l’hypothèse de conservation des niveaux de la fonction implicite associée à l’ensemble de niveaux et l’exprimons comme une énergie dans un formalisme variationnel. Cette énergie est ensuite ajoutée à l’algorithme décrit précédemment pour la segmentation statique du myocarde afin de contraindre temporellement l’évolution du contour. L’algorithme est alors validé sur 20 séquences échocardiographiques (soit environ 1200 images) segmentées par 2 experts

CREASEG: A free software for the evaluation of image segmentation algorithms based on level-set

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Multi-layer Ontologies for Integrated 3D Shape Segmentation and Annotation

International audienceMesh segmentation and semantic annotation are used as preprocessing steps for many applications, including shape retrieval, mesh abstraction, and adap-tive simplification. In current practice, these two steps are done sequentially: a purely geometrical analysis is employed to extract the relevant parts, and then these parts are annotated. We introduce an original framework where annotation and seg-mentation are performed simultaneously, so that each of the two steps can take advantage of the other. Inspired by existing methods used in image processing, we employ an expert's knowledge of the context to drive the process while minimizing the use of geometric analysis. For each specific context a multi-layer ontology can be designed on top of a basic knowledge layer which conceptualizes 3D object features from the point of view of their geometry, topology, and possible attributes. Each feature is associated with an elementary algorithm for its detection. An expert can define the upper layers of the ontology to conceptualize a specific domain without the need to reconsider the elementary algorithms. This approach has a twofold advantage: on one hand it allows to leverage domain knowledge from experts even if they have limited or no skills in geometry processing and computer program-Thomas Dietenbeck Sorbonne Universités, UPMC Univ Paris 06, INSERM UMRS 1146, CNRS UMR 7371, Labora-toire d'Imagerie Biomédicale, F-75013

愛與死的間繫:關於在我墳上起舞一書

International audienceMesh segmentation and annotation using semantics has received an increased interest with the recent democratisation of 3D reconstruction methods. The common approach is to perform this task in two steps, by first segmenting the mesh and then annotating it. However, this approach does not allow one part to take advantage of the other. In image processing, some methods are combining segmentation and annotation, but they are not generic and require implementation adjustments or rewritings for each modification of the expert knowledge. In this work, we describe an original framework that mixes segmen-tation and annotation while minimizing the required geometric analysis and we give preliminary results showing its feasability. Our framework provides a generic ontology describing object feature concepts (geometry, topology, etc.) and algorithms allowing to detect these concepts. This ontology can be enlarged by any expert to formally describe a specific do-main. The formalized domain description is then used to automatically perform the joint segmentation and annotation of objects and their features, by selecting at each step the most relevant algorithm given the previously detected seman-tics. This methodology has several advantages. Firsly it allows to segment and annotate objects without any knowledge in mesh or image processing by sim-ply describing the object features in terms of ontological concepts. Secondly this framework can be easily reused and applied to different contexts by sim-ply building on our generic ontology. Finally performing the joint segmentation and annotation allows to use in an efficient way the expert knowledge, reducing possible segmentation errors and the computation time by always launching the most efficient algorithm

Fast and fully automatic 3D echocardiographic segmentation using B-spline explicit active surfaces

International audienceWe have recently introduced a novel framework to efficiently deal with 3D segmentation of challenging inhomogeneous data in real-time. However, the existing framework still relied on manual initialization, which prevented taking full advantage of the computational speed of the method. In the present manuscript we propose an automatic initialization scheme adapted to 3D echocardiographic data and we couple it with the existing segmentation framework. Moreover, a novel segmentation functional, which explicitly takes the darker appearance of the blood into account, is also proposed in the present manuscript. We show that fully automatic segmentation of the left ventricle using the proposed method provides an efficient, fast and accurate solution for quantification of the main cardiac indices used in routine clinical practice

Non-invasive evaluation of retinal vascular remodeling and hypertrophy in humans: intricate effect of ageing, blood pressure and glycaemia

International audienceBackground: Ageing, hypertension and diabetes have an intricate effect on microvascular structure. In the retina, the respective contribution of remodeling and hypertrophy in such process is still unclear. We aimed at disentangling age, blood pressure and glycaemia effects on retinal microcirculation using the non-invasive adaptive optics ophthalmoscopy (AOO).Methods: We included 429 subjects, distributed into 4 groups according to normal (nBP) or high blood pressure (hBP) and/or normal (nGly) or high fasting glycaemia (hGly). The nBP/nGly group was stratified in age tertiles to isolate the effect of ageing. AOO was used to measure arteriolar wall thickness (WT, µm), arteriolar (aID, µm) and venular internal diameter (vID, µm) and calculate arteriolar wall-to-lumen ratio (WLR), wall cross-sectional area (WCSA, µm2). One-way ANOVA for parametric variables and Kruskal-Wallis test for non-parametric variables were used for comparison among groups. A multivariate regression analysis including age, gender, BP, hGly and antihypertensive treatment was performed to calculate independent predictors of retinal remodeling.Results: WT was increased with ageing (tertile1: 22.5 ± 3.2, tertile2: 24.2 ± 3.5, tertile 3: 25.2 ± 3.8, p = 0.001) and BP (hBP: 25.2 ± 4.1 vs nBP: 23.9 ± 3.7, p = 0.003). aID decreased with BP (hBP: 90.2 ± 13.4 vs nBP: 93.6 ± 11.6, p = 0.013) and increased with glycaemia (hGly: 97.7 ± 12.5 vs nGly: 93.6 ± 11.6, p = 0.002). A multivariate analysis showed independent association of hBP with WLR; hGly with WCSA; ageing with WLR and WCSA.Conclusions: AOO non-invasively identifies retinal structural changes in human confirming that microvascular remodeling is exclusively related to hypertension, whereas vascular growth is related to ageing and hyperglycaemia

Neuromuscular Fiber Segmentation Through Particle Filtering and Discrete Optimization

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Towards real-time 3D region-based segmentation: B-spline explicit active surfaces

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