Left-ventricular epi- and endocardium extraction from 3D ultrasound images using an automatically constructed 3D ASM

Butakoff, Constantine; Balocco, Simone; Sukno, Federico M.; Hoogendoorn, Corné; Tobon-Gómez, Catalina; Avegliano, Gustavo; Frangi, Alejandro F.

doi:10.1080/21681163.2014.910703

Cited by 6 publications

(4 citation statements)

References 38 publications

(49 reference statements)

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“…The PDM was built from 134 patients in 15 cardiac phases, totaling 2010 training volumes [28] , [29] . The segmentation algorithm has been trained and evaluated on CT [28] , SPECT [30] , 3D US [31] and MRI [32] datasets.…”

Section: Methods and Proceduresmentioning

confidence: 99%

Pre to Intraoperative Data Fusion Framework for Multimodal Characterization of Myocardial Scar Tissue

Porras

Piella

Berruezo

et al. 2014

IEEE J. Transl. Eng. Health Med.

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Merging multimodal information about myocardial scar tissue can help electrophysiologists to find the most appropriate target during catheter ablation of ventricular arrhythmias. A framework is presented to analyze and combine information from delayed enhancement magnetic resonance imaging (DE-MRI) and electro-anatomical mapping data. Using this information, electrical, mechanical, and image-based characterization of the myocardium are performed. The presented framework allows the left ventricle to be segmented by DE-MRI and the scar to be characterized prior to the intervention based on image information. It allows the electro-anatomical maps obtained during the intervention from a navigation system to be merged together with the anatomy and scar information extracted from DE-MRI. It also allows for the estimation of endocardial motion and deformation to assess cardiac mechanics. Therefore, electrical, mechanical, and image-based characterization of the myocardium can be performed. The feasibility of this approach was demonstrated on three patients with ventricular tachycardia associated to ischemic cardiomyopathy by integrating images from DE-MRI and electro-anatomical maps data in a common framework for intraoperative myocardial tissue characterization. The proposed framework has the potential to guide and monitor delivery of radio frequency ablation of ventricular tachycardia. It is also helpful for research purposes, facilitating the study of the relationship between electrical and mechanical properties of the tissue, as well as with tissue viability from DE-MRI.

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Section: Methods and Proceduresmentioning

confidence: 99%

Pre to Intraoperative Data Fusion Framework for Multimodal Characterization of Myocardial Scar Tissue

Porras

Piella

Berruezo

et al. 2014

IEEE J. Transl. Eng. Health Med.

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“…Step 1: 3D shape modeling using surface parameterization 4 forall voxels v in cuboid lattice l do 5 Step 2: Initialize self-similarity stochastic process Normalize voxel pairs distances ∆r;…”

Section: Classificationmentioning

confidence: 99%

Spatio-Temporal Segmentation in 3-D Echocardiographic Sequences Using Fractional Brownian Motion

Al-Kadi

2020

IEEE Trans. Biomed. Eng.

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An important aspect for an improved cardiac functional analysis is the accurate segmentation of the left ventricle (LV). A novel approach for fully-automated segmentation of the LV endocardium and epicardium contours is presented. This is mainly based on the natural physical characteristics of the LV shape structure. Both sides of the LV boundaries exhibit natural elliptical curvatures by having details on various scales, i.e. exhibiting fractal-like characteristics. The fractional Brownian motion (fBm), which is a non-stationary stochastic process, integrates well with the stochastic nature of ultrasound echoes. It has the advantage of representing a wide range of non-stationary signals and can quantify statistical local self-similarity throughout the time-sequence ultrasound images. The locally characterized boundaries of the fBm segmented LV were further iteratively refined using global information by means of second-order moments. The method is benchmarked using synthetic 3D+time echocardiographic sequences for normal and different ischemic cardiomyopathy, and results compared with state-of-the-art LV segmentation. Furthermore, the framework was validated against real data from canine cases with expert-defined segmentations and demonstrated improved accuracy. The fBm-based segmentation algorithm is fully automatic and has the potential to be used clinically together with 3D echocardiography for improved cardiovascular disease diagnosis.

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“…The model is then deform to fit an image in ways similar to the variations observed in the training set. Butakoff et al [19] uses an automatically constructed ASM for myocardial segmentation in 3D B-mode echo. Pickard et al [22] uses ASM for MCE myocardial segmentation and they apply a specialized gradient vector flow field to increase its edged capture range.…”

Section: Related Workmentioning

confidence: 99%

“…In this section, we give a brief overview of the segmentation methods used for cardiac ultrasound and highlight their limitations when applied to our MCE data. Current literature focus mainly on either LV segmentation [9]- [15] or myocardial segmentation [4], [16]- [19] in B-mode echocardiography. There are fewer of them addressing segmentation in MCE [3], [20]- [22].…”

Section: Related Workmentioning

confidence: 99%

Fully Automatic Myocardial Segmentation of Contrast Echocardiography Sequence Using Random Forests Guided by Shape Model

Li¹,

Ho²,

Toulemonde³

et al. 2018

IEEE Trans. Med. Imaging

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Myocardial contrast echocardiography (MCE) is an imaging technique that assesses left ventricle function and myocardial perfusion for the detection of coronary artery diseases. Automatic MCE perfusion quantification is challenging and requires accurate segmentation of the myocardium from noisy and time-varying images. Random forests (RF) have been successfully applied to many medical image segmentation tasks. However, the pixel-wise RF classifier ignores contextual relationships between label outputs of individual pixels. RF which only utilizes local appearance features is also susceptible to data suffering from large intensity variations. In this paper, we demonstrate how to overcome the above limitations of classic RF by presenting a fully automatic segmentation pipeline for myocardial segmentation in full-cycle 2-D MCE data. Specifically, a statistical shape model is used to provide shape prior information that guide the RF segmentation in two ways. First, a novel shape model (SM) feature is incorporated into the RF framework to generate a more accurate RF probability map. Second, the shape model is fitted to the RF probability map to refine and constrain the final segmentation to plausible myocardial shapes. We further improve the performance by introducing a bounding box detection algorithm as a preprocessing step in the segmentation pipeline. Our approach on 2-D image is further extended to 2-D+t sequences which ensures temporal consistency in the final sequence segmentations. When evaluated on clinical MCE data sets, our proposed method achieves notable improvement in segmentation accuracy and outperforms other state-of-the-art methods, including the classic RF and its variants, active shape model and image registration.

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Left-ventricular epi- and endocardium extraction from 3D ultrasound images using an automatically constructed 3D ASM

Cited by 6 publications

References 38 publications

Pre to Intraoperative Data Fusion Framework for Multimodal Characterization of Myocardial Scar Tissue

Pre to Intraoperative Data Fusion Framework for Multimodal Characterization of Myocardial Scar Tissue

Spatio-Temporal Segmentation in 3-D Echocardiographic Sequences Using Fractional Brownian Motion

Fully Automatic Myocardial Segmentation of Contrast Echocardiography Sequence Using Random Forests Guided by Shape Model

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