Semiautomated biventricular segmentation in three-dimensional echocardiography by coupled deformable surfaces

Abstract

With the advancement of three-dimensional (3-D) real-time echocardiography in recent years, automatic creation of patient specific geometric models is becoming feasible and important in clinical decision making. However, the vast majority of echocardiographic segmentation methods presented in the literature focus on the left ventricle (LV) endocardial border, leaving segmentation of the right ventricle (RV) a largely unexplored problem, despite the increasing recognition of the RV's role in cardiovascular disease. We present a method for coupled segmentation of the endo- and epicardial borders of both the LV and RV in 3-D ultrasound images. To solve the segmentation problem, we propose an extension of a successful state-estimation segmentation framework with a geometrical representation of coupled surfaces, as well as the introduction of myocardial incompressibility to regularize the segmentation. The method was validated against manual measurements and segmentations in images of 16 patients. Mean absolute distances of [Formula: see text], [Formula: see text], and [Formula: see text] between the proposed and reference segmentations were observed for the LV endocardium, RV endocardium, and LV epicardium surfaces, respectively. The method was computationally efficient, with a computation time of [Formula: see text].

Signal dropout correction-based ultrasound segmentation for diastolic mitral valve modeling

“…From Table 1, we see that the proposed segmentation algorithm improved the accuracy in terms of evaluation values: DSC, MAD, and MAXD, comparing with the CMF + AC algorithm. In contrast to existing segmentation results of MV images with signal dropout, 10,13 our segmentation results gave further improvement. As an example shown in Fig.…”

“…11 Baxter et al 12 analyzed an optimization-based interactive segmentation interface for multiregion problems. Bersvendsen et al 13 proposed a semiautomated segmentation for 3-D echocardiography. These image segmentation methods provide the practicality of MV quantification by 3-D TEE, but MV leaflet segmentation is challenging due to irregular leaflet motion throughout cardiac cycle.…”

Artificial intelligence in echocardiography: detection, functional evaluation, and disease diagnosis

“…In addition, Bersvendsen et al proposed and constructed a multi-chamber model based on how the chambers interact while performing the pumping function, which allows for coupled segmentation of endo- and epicardial borders of the left and right ventricle. The establishment of the multi-chamber model can allow for a complete clinical condition evaluation [ 24 ]. In general, when the image quality is poor, the precision of right ventricular segmentation is dependent on previously obtained information and correlation with other heart structures, such as myocardium, epicardium, and left ventricle.…”

Automatic alignment of standard views for transesophageal echocardiographic images

“…Doo–Sabin surfaces have also previously been used for cardiac modeling, for instance, by Orderud, 14 Dikici, 15 and Bølviken et al 16 . The Kalman filter is an algorithm for finding unknown variables based both on model-based predictions and measurements and has previously been used for segmentation of cardiac ultrasound images by Smistad and Lindseth 17 and Bersvendsen, 18 among others.…”