Fully automatic segmentation of the mitral leaflets in 3D transesophageal echocardiographic images using multi-atlas joint label fusion and deformable medial modeling
Abstract:Comprehensive visual and quantitative analysis of in vivo human mitral valve morphology is central to the diagnosis and surgical treatment of mitral valve disease. Real-time 3D transesophageal echocardiography (3D TEE) is a practical, highly informative imaging modality for examining the mitral valve in a clinical setting. To facilitate visual and quantitative 3D TEE image analysis, we describe a fully automated method for segmenting the mitral leaflets in 3D TEE image data. The algorithm integrates complement… Show more
“…The template medial mesh, shown in Fig. 1b, is generated using a method similar to that described in [8] and is deformed to maximize its overlap with the target segmentation while imposing soft regularization constraints to ensure mesh regularity and validity of medial axis geometry as described in [8]. …”
Transesophageal echocardiography is the primary imaging modality for preoperative assessment of mitral valves with ischemic mitral regurgitation (IMR). While there are well known echocardiographic insights into the 3D morphology of mitral valves with IMR, such as annular dilation and leaflet tethering, less is understood about how quantification of valve dynamics can inform surgical treatment of IMR or predict short-term recurrence of the disease. As a step towards filling this knowledge gap, we present a novel framework for 4D segmentation and geometric modeling of the mitral valve in real-time 3D echocardiography (rt-3DE). The framework integrates multi-atlas label fusion and template-based medial modeling to generate quantitatively descriptive models of valve dynamics. The novelty of this work is that temporal consistency in the rt-3DE segmentations is enforced during both the segmentation and modeling stages with the use of groupwise label fusion and Kalman filtering. The algorithm is evaluated on rt-3DE data series from 10 patients: five with normal mitral valve morphology and five with severe IMR. In these 10 data series that total 207 individual 3DE images, each 3DE segmentation is validated against manual tracing and temporal consistency between segmentations is demonstrated. The ultimate goal is to generate accurate and consistent representations of valve dynamics that can both visually and quantitatively provide insight into normal and pathological valve function.
“…The template medial mesh, shown in Fig. 1b, is generated using a method similar to that described in [8] and is deformed to maximize its overlap with the target segmentation while imposing soft regularization constraints to ensure mesh regularity and validity of medial axis geometry as described in [8]. …”
Transesophageal echocardiography is the primary imaging modality for preoperative assessment of mitral valves with ischemic mitral regurgitation (IMR). While there are well known echocardiographic insights into the 3D morphology of mitral valves with IMR, such as annular dilation and leaflet tethering, less is understood about how quantification of valve dynamics can inform surgical treatment of IMR or predict short-term recurrence of the disease. As a step towards filling this knowledge gap, we present a novel framework for 4D segmentation and geometric modeling of the mitral valve in real-time 3D echocardiography (rt-3DE). The framework integrates multi-atlas label fusion and template-based medial modeling to generate quantitatively descriptive models of valve dynamics. The novelty of this work is that temporal consistency in the rt-3DE segmentations is enforced during both the segmentation and modeling stages with the use of groupwise label fusion and Kalman filtering. The algorithm is evaluated on rt-3DE data series from 10 patients: five with normal mitral valve morphology and five with severe IMR. In these 10 data series that total 207 individual 3DE images, each 3DE segmentation is validated against manual tracing and temporal consistency between segmentations is demonstrated. The ultimate goal is to generate accurate and consistent representations of valve dynamics that can both visually and quantitatively provide insight into normal and pathological valve function.
“…1c), and deformable registration [19] was used to propagate that segmentation to all the other frames in the image series. Further details on the segmentation technique are beyond the scope of this paper, and the reader is referred to [17] for more information.Fig. 1From rt-TEE ultrasound images to segmented triangulated surfaces.…”
BackgroundThe goal of this paper is to present a computational fluid dynamic (CFD) model with moving boundaries to study the intraventricular flows in a patient-specific framework. Starting from the segmentation of real-time transesophageal echocardiographic images, a CFD model including the complete left ventricle and the moving 3D mitral valve was realized. Their motion, known as a function of time from the segmented ultrasound images, was imposed as a boundary condition in an Arbitrary Lagrangian–Eulerian framework.ResultsThe model allowed for a realistic description of the displacement of the structures of interest and for an effective analysis of the intraventricular flows throughout the cardiac cycle. The model provides detailed intraventricular flow features, and highlights the importance of the 3D valve apparatus for the vortex dynamics and apical flow.ConclusionsThe proposed method could describe the haemodynamics of the left ventricle during the cardiac cycle. The methodology might therefore be of particular importance in patient treatment planning to assess the impact of mitral valve treatment on intraventricular flow dynamics.Electronic supplementary materialThe online version of this article (doi:10.1186/s12938-016-0231-9) contains supplementary material, which is available to authorized users.
“…Finally, there are many other applications that have benefited from MAS within human medical imaging, including: segmentation of pelvic bones in MRI (Weisenfeld and Warfield, 2011b; Akhondi-Asl et al, 2014); lungs in CT scans (van Rikxoort et al, 2009) and chest X-rays (Candemir et al, 2014); heart and its ventricles in CT (van Rikxoort et al, 2010; Dey et al, 2010), MRI (Zhuang et al, 2010; Zuluaga et al, 2014), MR angiography (Wachinger and Golland, 2012), ultrasound (Wang et al, 2014a), and CT angiography (Kirişli et al, 2010; Yang et al, 2014a); breast tissues and lesions in X-ray mammography (Iglesias and Karssemeijer, 2009) and MRI (Gubern-Mérida et al, 2012; Lee et al, 2013); cartilage and bone in knee MRI (Tamez-Pena et al, 2012; Lee et al, 2014b; Shan et al, 2014); the vertebrae in spinal MRI (Asman et al, 2014); scar tissue in intravascular coronary optical coherence tomography (OCT) (Tung et al, 2013); the mitral valve in transesophageal echocardiography (Wang et al, 2013a; Pouch et al, 2014); skeletal muscle in whole-body MRI (Karlsson et al, 2014); kidneys in CT images (Yang et al, 2014b); and bone in dental cone-beam CT images (Wang et al, 2014c). …”
Multi-atlas segmentation (MAS), first introduced and popularized by the pioneering work of Rohlfing, Brandt, Menzel and Maurer Jr (2004), Klein, Mensh, Ghosh, Tourville and Hirsch (2005), and Heckemann, Hajnal, Aljabar, Rueckert and Hammers (2006), is becoming one of the most widely-used and successful image segmentation techniques in biomedical applications. By manipulating and utilizing the entire dataset of “atlases” (training images that have been previously labeled, e.g., manually by an expert), rather than some model-based average representation, MAS has the flexibility to better capture anatomical variation, thus offering superior segmentation accuracy. This benefit, however, typically comes at a high computational cost. Recent advancements in computer hardware and image processing software have been instrumental in addressing this challenge and facilitated the wide adoption of MAS. Today, MAS has come a long way and the approach includes a wide array of sophisticated algorithms that employ ideas from machine learning, probabilistic modeling, optimization, and computer vision, among other fields. This paper presents a survey of published MAS algorithms and studies that have applied these methods to various biomedical problems. In writing this survey, we have three distinct aims. Our primary goal is to document how MAS was originally conceived, later evolved, and now relates to alternative methods. Second, this paper is intended to be a detailed reference of past research activity in MAS, which now spans over a decade (2003 – 2014) and entails novel methodological developments and application-specific solutions. Finally, our goal is to also present a perspective on the future of MAS, which, we believe, will be one of the dominant approaches in biomedical image segmentation.
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