Segmentation of the heart and great vessels in CT images using a model-based adaptation framework

Ecabert, Olivier; Peters, Jörg; Walker, Matthew J.; Ivanc, Thomas B.; Lorenz, Cristian; Berg, Jens von; Lessick, Jonathan; Vembar, Mani; Weese, Jürgen

doi:10.1016/j.media.2011.06.004

Cited by 97 publications

(96 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A first evaluation of the proposed competitive approach was performed on atrial region segmentation in CT images. The obtained results proved the high accuracy of the method, with errors similar to the ones found in other studies in literature (Ecabert et al, 2011;Kirişli et al, 2010;Zheng et al, 2008), particularly for LA ( Figure 9) and aortic tract (Table 3). Indeed, the proposed double-stage segmentation approach (i.e.…”

Section: Discussionsupporting

confidence: 88%

“…To the author's best knowledge, no previous work was presented for accurate segmentation of the atrial region with intact mid-thin walls, being a clear novelty of this work. Previous works as (Ecabert et al, 2011) and(Zuluaga et al, 2013) simply merge the different contours (if overlap happens) or prevented gap/vacuum regions, being sub-optimal strategies for clinical evaluation of these thin regions. Although no significant differences are expected between the merged contour strategies and our approach in terms of segmentation evaluation metrics (e.g.…”

Section: Discussionmentioning

confidence: 99%

“…Automatic and semi-automatic atrial region segmentation solutions have been explored and presented for multiple imaging modalities, such as computed tomography (CT, (Ecabert et al, 2011;Kirişli et al, 2010;Zheng et al, 2008)) and magnetic resonance imaging (MRI, (Zuluaga et al, 2013)). Since the manual approach is tedious, time-consuming and has a high intra-and inter-observer variability, automated segmentation processes have been widely explored and increasingly accepted in normal clinical practice.…”

Section: Introductionmentioning

confidence: 99%

“…Since the manual approach is tedious, time-consuming and has a high intra-and inter-observer variability, automated segmentation processes have been widely explored and increasingly accepted in normal clinical practice. The majority of the applied methods are based on deformable models (Ecabert et al, 2011), atlas-based techniques (Kirişli et al, 2010;Zuluaga et al, 2013) and machine learning (Zheng et al, 2008), proving its high accuracy in a high number of cases with different pathologies. Some studies focused only on the left atrium (LA) due to its importance for atrial fibrillation, using a simple initialization strategy through a multi-atlas to obtain a rough contour alignment (Sandoval et al, 2013) or a probabilistic atlas (Stender et al, 2013), followed by a region growing and multiple 2D individual segmentations with circular shape descriptors (Ammar et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, the atlasbased technique with a final majority voting per chamber approach presented by (Kirişli et al, 2010) was unable to prevent overlap between contours. Contrarily, the atlas-based approach from (Zuluaga et al, 2013) and the deformable model proposed by (Ecabert et al, 2011) prevented overlapping regions by simply merging these regions. Thus, accurate assessment of the thin walls is currently not possible, missing their use for specific diagnostic purposes and interventional planning related with the thin atrial walls, e.g.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A competitive strategy for atrial and aortic tract segmentation based on deformable models

Morais

Vilaça

Queirós

et al. 2017

Medical Image Analysis

View full text Add to dashboard Cite

A competitive strategy for atrial and aortic tract segmentation based on deformable models AbstractMultiple strategies have previously been described for atrial region (i.e. atrial bodies and aortic tract) segmentation. Although these techniques have proven their accuracy, inadequate results in the mid atrial walls are common, restricting their application for specific cardiac interventions. In this work, we introduce a novel competitive strategy to perform atrial region segmentation with correct delineation of the thin mid walls, and integrated it into the B-spline Explicit Active Surfaces framework. A double-stage segmentation process is used, which starts with a fast contour growing followed by a refinement stage with local descriptors. Independent functions are used to define each region, being afterward combined to compete for the optimal boundary. The competition locally constrains the surface evolution, prevents overlaps and allows refinement to the walls. Three different scenarios were used to demonstrate the advantages of the proposed approach, through the evaluation of its segmentation accuracy, and its performance for heterogeneous mid walls. Both computed tomography and magnetic resonance imaging datasets were used, presenting results similar to the state-of-the-art methods for both atria and aorta. The competitive strategy showed its superior performance with statistically significant differences against the traditional freeevolution approach in cases with bad image quality or missed atrial/aortic walls. Moreover, only the competitive approach was able to accurately segment the atrial/aortic wall. Overall, the proposed strategy showed to be suitable for atrial region segmentation with a correct segmentation of the mid thin walls, demonstrating its added value with respect to the traditional techniques.

show abstract

Section: Discussionsupporting

confidence: 88%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A competitive strategy for atrial and aortic tract segmentation based on deformable models

Morais

Vilaça

Queirós

et al. 2017

Medical Image Analysis

View full text Add to dashboard Cite

show abstract

Personalization of Electromechanical Models of the Cardiac Ventricular Function by Heterogeneous Clinical Data Assimilation

Marchesseau¹,

Sermesant²,

Billet³

et al. 2015

Multi‐Modality Cardiac Imaging

View full text Add to dashboard Cite

Combining statistical shape modeling, CFD, and meta‐modeling to approximate the patient‐specific pressure‐drop across the aortic valve in real‐time

Hoeijmakers

Waechter-Stehle

Weese

et al. 2020

Numer Methods Biomed Eng

View full text Add to dashboard Cite

Background: Advances in medical imaging, segmentation techniques, and high performance computing have stimulated the use of complex, patient-specific, three-dimensional Computational Fluid Dynamics (CFD) simulations. Patient-specific, CFD-compatible geometries of the aortic valve are readily obtained. CFD can then be used to obtain the patient-specific pressure-flow relationship of the aortic valve. However, such CFD simulations are computationally expensive, and real-time alternatives are desired. Aim: The aim of this work is to evaluate the performance of a meta-model with respect to high-fidelity, three-dimensional CFD simulations of the aortic valve. Methods: Principal component analysis was used to build a statistical shape model (SSM) from a population of 74 iso-topological meshes of the aortic valve. Synthetic meshes were created with the SSM, and steady-state CFD simulations at flow-rates between 50 and 650 mL/s were performed to build a metamodel. The meta-model related the statistical shape variance, and flow-rate to the pressure-drop. Results: Even though the first three shape modes account for only 46% of shape variance, the features relevant for the pressure-drop seem to be captured. The three-mode shape-model approximates the pressure-drop with an average error of 8.8% to 10.6% for aortic valves with a geometric orifice area below 150 mm 2. The proposed methodology was least accurate for aortic valve areas above 150 mm 2. Further reduction to a meta-model introduces an additional 3% error.

show abstract

Segmentation of the heart and great vessels in CT images using a model-based adaptation framework

Cited by 97 publications

References 36 publications

A competitive strategy for atrial and aortic tract segmentation based on deformable models

A competitive strategy for atrial and aortic tract segmentation based on deformable models

Personalization of Electromechanical Models of the Cardiac Ventricular Function by Heterogeneous Clinical Data Assimilation

Combining statistical shape modeling, CFD, and meta‐modeling to approximate the patient‐specific pressure‐drop across the aortic valve in real‐time

Contact Info

Product

Resources

About