Mitral annulus segmentation from 3D ultrasound using graph cuts

A simulation-based phantom model for generating synthetic mitral valve image data-application to MRI acquisition planning

PURPOSE

Numerical phantom methods are widely used in the development of medical imaging methods. They enable quantitative evaluation and direct comparison with controlled and known ground truth information. Cardiac magnetic resonance has the potential for a comprehensive evaluation of the mitral valve (MV). The goal of this work is the development of a numerical simulation framework that supports the investigation of MRI imaging strategies for the mitral valve.

METHODS

We present a pipeline for synthetic image generation based on the combination of individual anatomical 3D models with a position-based dynamics simulation of the mitral valve closure. The corresponding images are generated using modality-specific intensity models and spatiotemporal sampling concepts. We test the applicability in the context of MRI imaging strategies for the assessment of the mitral valve. Synthetic images are generated with different strategies regarding image orientation (SAX and rLAX) and spatial sampling density.

RESULTS

The suitability of the imaging strategy is evaluated by comparing MV segmentations against ground truth annotations. The generated synthetic images were compared to ones acquired with similar parameters, and the result is promising. The quantitative analysis of annotation results suggests that the rLAX sampling strategy is preferable for MV assessment, reaching accuracy values that are comparable to or even outperform literature values.

CONCLUSION

The proposed approach provides a valuable tool for the evaluation and optimization of cardiac valve image acquisition. Its application to the use case identifies the radial image sampling strategy as the most suitable for MV assessment through MRI.

Automated estimation of mitral annular plane systolic excursion by artificial intelligence from 3D ultrasound recordings

Perioperative monitoring of cardiac function is beneficial for early detection of cardiovascular complications. The standard of care for cardiac monitoring performed by trained cardiologists and anesthesiologists involves a manual and qualitative evaluation of ultrasound imaging, which is a time-demanding and resource-intensive process with intraobserver- and interobserver variability. In practice, such measures can only be performed a limited number of times during the intervention. To overcome these difficulties, this study presents a robust method for automatic and quantitative monitoring of cardiac function based on 3D transesophageal echocardiography (TEE) B-mode ultrasound recordings of the left ventricle (LV). Such an assessment obtains consistent measurements and can produce a near real-time evaluation of ultrasound imagery. Hence, the presented method is time-saving and results in increased accessibility. The mitral annular plane systolic excursion (MAPSE), characterizing global LV function, is estimated by landmark detection and cardiac view classification of two-dimensional images extracted along the long-axis of the ultrasound volume. MAPSE estimation directly from 3D TEE recordings is beneficial since it removes the need for manual acquisition of cardiac views, hence decreasing the need for interference by physicians. Two convolutional neural networks (CNNs) were trained and tested on acquired ultrasound data of 107 patients, and MAPSE estimates were compared to clinically obtained references in a blinded study including 31 patients. The proposed method for automatic MAPSE estimation had low bias and low variability in comparison to clinical reference measures. The method accomplished a mean difference for MAPSE estimates of (-0.16±1.06) mm. Thus, the results did not show significant systematic errors. The obtained bias and variance of the method were comparable to inter-observer variability of clinically obtained MAPSE measures on 2D TTE echocardiography. The novel pipeline proposed in this study has the potential to enhance cardiac monitoring in perioperative- and intensive care settings.

Transfer learning for automatic joint segmentation of thyroid and breast lesions from ultrasound images

PURPOSE

It plays a significant role to accurately and automatically segment lesions from ultrasound (US) images in clinical application. Nevertheless, it is extremely challenging because distinct components of heterogeneous lesions are similar to background in US images. In our study, a transfer learning-based method is developed for full-automatic joint segmentation of nodular lesions.

METHODS

Transfer learning is a widely used method to build high performing computer vision models. Our transfer learning model is a novel type of densely connected convolutional network (SDenseNet). Specifically, we pre-train SDenseNet based on ImageNet dataset. Then our SDenseNet is designed as a multi-channel model (denoted Mul-DenseNet) for automatically jointly segmenting lesions. As comparison, our SDenseNet using different transfer learning is applied to segmenting nodules, respectively. In our study, we find that more datasets for pre-training and multiple pre-training do not always work in segmentation of nodules, and the performance of transfer learning depends on a judicious choice of dataset and characteristics of targets.

RESULTS

Experimental results illustrate a significant performance of the Mul-DenseNet compared to that of other methods in the study. Specially, for thyroid nodule segmentation, overlap metric (OM), dice ratio (DR), true-positive rate (TPR), false-positive rate (FPR) and modified Hausdorff distance (MHD) are [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] mm, respectively; for breast nodule segmentation, OM, DR, TPR, FPR and MHD are [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] mm, respectively.

CONCLUSIONS

The experimental results illustrate our transfer learning models are very effective in segmentation of lesions, which also demonstrate that it is potential of our proposed Mul-DenseNet model in clinical applications. This model can reduce heavy workload of the physicians so that it can avoid misdiagnosis cases due to excessive fatigue. Moreover, it is easy and reproducible to detect lesions without medical expertise.

Unsupervised Mitral Valve Tracking for Disease Detection in Echocardiogram Videos

In this work, a novel algorithmic scheme is developed that processes echocardiogram videos, and tracks the movement of the mitral valve leaflets, and thereby estimates whether the movement is symptomatic of a healthy or diseased heart. This algorithm uses automatic Otsu's thresholding to find a closed boundary around the left atrium, with the basic presumption that it is situated in the bottom right corner of the apical 4 chamber view. A centroid is calculated, and protruding prongs are taken within a 40-degree cone above the centroid, where the mitral valve is located. Binary images are obtained from the videos where the mitral valve leaflets have different pixel values than the cavity of the left atrium. Thus, the points where the prongs touch the valve will show where the mitral valve leaflets are located. The standard deviation of these points is used to calculate closeness of the leaflets. The estimation of the valve movement across subsequent frames is used to determine if the movement is regular, or affected by heart disease. Tests conducted with numerous videos containing both healthy and diseased hearts attest to our method's efficacy, with a key novelty in being fully unsupervised and computationally efficient.

Artificial intelligence and automation in valvular heart diseases

Artificial intelligence (AI) is gradually changing every aspect of social life, and healthcare is no exception. The clinical procedures that were supposed to, and could previously only be handled by human experts can now be carried out by machines in a more accurate and efficient way. The coming era of big data and the advent of supercomputers provides great opportunities to the development of AI technology for the enhancement of diagnosis and clinical decision-making. This review provides an introduction to AI and highlights its applications in the clinical flow of diagnosing and treating valvular heart diseases (VHDs). More specifically, this review first introduces some key concepts and subareas in AI. Secondly, it discusses the application of AI in heart sound auscultation and medical image analysis for assistance in diagnosing VHDs. Thirdly, it introduces using AI algorithms to identify risk factors and predict mortality of cardiac surgery. This review also describes the state-of-the-art autonomous surgical robots and their roles in cardiac surgery and intervention.

Mitral Annulus Segmentation Using Deep Learning in 3-D Transesophageal Echocardiography

3D Transesophageal Echocardiography is an excellent tool for evaluating the mitral valve and is also well suited for guiding cardiac interventions. We introduce a fully automatic method for mitral annulus segmentation in 3D Transesophageal Echocardiography, which requires no manual input. One hundred eleven multi-frame 3D transesophageal echocardiography recordings were split into training, validation, and test sets. Each 3D recording was decomposed into a set of 2D planes, exploiting the symmetry around the centerline of the left ventricle. A deep 2D convolutional neural network was trained to predict the mitral annulus coordinates, and the predictions from neighboring planes were regularized by enforcing continuity around the annulus. Applying the final model and post-processing to the test set data gave a mean error of 2.0 mm - with a standard deviation of 1.9 mm. Fully automatic segmentation of the mitral annulus can alleviate the need for manual interaction in the quantification of an array of mitral annular parameters and has the potential to eliminate inter-observer variability.

Combining position-based dynamics and gradient vector flow for 4D mitral valve segmentation in TEE sequences

PURPOSE

For planning and guidance of minimally invasive mitral valve repair procedures, 3D+t transesophageal echocardiography (TEE) sequences are acquired before and after the intervention. The valve is then visually and quantitatively assessed in selected phases. To enable a quantitative assessment of valve geometry and pathological properties in all heart phases, as well as the changes achieved through surgery, we aim to provide a new 4D segmentation method.

METHODS

We propose a tracking-based approach combining gradient vector flow (GVF) and position-based dynamics (PBD). An open-state surface model of the valve is propagated through time to the closed state, attracted by the GVF field of the leaflet area. The PBD method ensures topological consistency during deformation. For evaluation, one expert in cardiac surgery annotated the closed-state leaflets in 10 TEE sequences of patients with normal and abnormal mitral valves, and defined the corresponding open-state models.

RESULTS

The average point-to-surface distance between the manual annotations and the final tracked model was [Formula: see text]. Qualitatively, four cases were satisfactory, five passable and one unsatisfactory. Each sequence could be segmented in 2-6 min.

CONCLUSION

Our approach enables to segment the mitral valve in 4D TEE image data with normal and pathological valve closing behavior. With this method, in addition to the quantification of the remaining orifice area, shape and dimensions of the coaptation zone can be analyzed and considered for planning and surgical result assessment.

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

Three-dimensional ultrasound segmentation of mitral valve (MV) at diastole is helpful for duplicating geometry and pathology in a patient-specific dynamic phantom. The major challenge is the signal dropout at leaflet regions in transesophageal echocardiography image data. Conventional segmentation approaches suffer from missing sonographic data leading to inaccurate MV modeling at leaflet regions. This paper proposes a signal dropout correction-based ultrasound segmentation method for diastolic MV modeling. The proposed method combines signal dropout correction, image fusion, continuous max-flow segmentation, and active contour segmentation techniques. The signal dropout correction approach is developed to recover the missing segmentation information. Once the signal dropout regions of TEE image data are recovered, the MV model can be accurately duplicated. Compared with other methods in current literature, the proposed algorithm exhibits lower computational cost. The experimental results show that the proposed algorithm gives competitive results for diastolic MV modeling compared with conventional segmentation algorithms, evaluated in terms of accuracy and efficiency.

Mitral valve annulus localization in 3D echocardiography

The Mitral Valve is a structure on the left side of the human heart that regulates the flow of oxygenated blood into the Left Ventricle and also helps maintain the pressure within the Left Ventricle when the blood gets pumped to the rest of the body from the Left Ventricle. Pathology of the Mitral Valve often manifests through structural changes in the anatomy. Assessment of Mitral Valve pathology as well as determination of specific interventions require quantification of various structures of the Mitral Valve and one of these structures of interest is the Mitral Valve annulus. Three dimensional echocardiography is a popular imaging technique that clinicians use for volumetric quantification of cardiac structures - but manual quantification is cumbersome and subjective. In this paper we describe a semi-automated approach for maximum-a-posteriori estimation of the Mitral Valve annulus from three-dimensional echocardiography images using a simple analytic shape model coupled with a Näive Bayes classifier. We validate our approach against manual annotations on over 15 real patient cases with an average localization error ≤ 2.59 mm.

Ultrasound guidance for beating heart mitral valve repair augmented by synthetic dynamic CT

Minimally invasive valvular intervention commonly requires intra-procedural navigation to provide spatial and temporal information of relevant cardiac structures and device components. Recently intra-procedural trans-esophageal echocardiography (TEE) has been exploited for this purpose due to its accessibility, low cost, ease of use, and real-time imaging capacity. However, the position and orientation of tissue targets relative to surgical tools can be challenging to perceive, particularly using 2D imaging planes. In this paper, we propose the use of CT images to provide a high-quality 3D context to enhance ultrasound images through image registration, providing an augmented guidance system with minimal impact on standard clinical workflow. We also describe an approach to generate synthetic 4D CT images through non-rigid registration of available ultrasound. This can be employed to avoid a requirement for higher radiation. Synthetic CT images were validated through direct comparison of synthetic and real multi-phase CT images. Validation of CT and ultrasound image registration was performed for both dynamic and synthetic CT image datasets. Our results demonstrated that the synthetically generated dynamic CT images provide similar anatomical representation for relevant cardiac anatomy relative to real dynamic CT images, and similar high registration accuracy that can be achieved for intra-procedural TEE to this versus real dynamic CT images.

Identification of Mitral Annulus Hinge Point Based on Local Context Feature and Additive SVM Classifier

The position of the hinge point of mitral annulus (MA) is important for segmentation, modeling and multimodalities registration of cardiac structures. The main difficulties in identifying the hinge point of MA are the inherent noisy, low resolution of echocardiography, and so on. This work aims to automatically detect the hinge point of MA by combining local context feature with additive support vector machines (SVM) classifier. The innovations are as follows: (1) designing a local context feature for MA in cardiac ultrasound image; (2) applying the additive kernel SVM classifier to identify the candidates of the hinge point of MA; (3) designing a weighted density field of candidates which represents the blocks of candidates; and (4) estimating an adaptive threshold on the weighted density field to get the position of the hinge point of MA and exclude the error from SVM classifier. The proposed algorithm is tested on echocardiographic four-chamber image sequence of 10 pediatric patients. Compared with the manual selected hinge points of MA which are selected by professional doctors, the mean error is in 0.96 ± 1.04 mm. Additive SVM classifier can fast and accurately identify the MA hinge point.

A mitral annulus tracking approach for navigation of off-pump beating heart mitral valve repair

PURPOSE

To develop and validate a real-time mitral valve annulus (MVA) tracking approach based on biplane transesophageal echocardiogram (TEE) data and magnetic tracking systems (MTS) to be used in minimally invasive off-pump beating heart mitral valve repair (MVR).

METHODS

The authors' guidance system consists of three major components: TEE, magnetic tracking system, and an image guidance software platform. TEE provides real-time intraoperative images to show the cardiac motion and intracardiac surgical tools. The magnetic tracking system tracks the TEE probe and the surgical tools. The software platform integrates the TEE image planes and the virtual model of the tools and the MVA model on the screen. The authors' MVA tracking approach, which aims to update the MVA model in near real-time, comprises of three steps: image based gating, predictive reinitialization, and registration based MVA tracking. The image based gating step uses a small patch centered at each MVA point in the TEE images to identify images at optimal cardiac phases for updating the position of the MVA. The predictive reinitialization step uses the position and orientation of the TEE probe provided by the magnetic tracking system to predict the position of the MVA points in the TEE images and uses them for the initialization of the registration component. The registration based MVA tracking step aims to locate the MVA points in the images selected by the image based gating component by performing image based registration.

RESULTS

The validation of the MVA tracking approach was performed in a phantom study and a retrospective study on porcine data. In the phantom study, controlled translations were applied to the phantom and the tracked MVA was compared to its "true" position estimated based on a magnetic sensor attached to the phantom. The MVA tracking accuracy was 1.29 ± 0.58 mm when the translation distance is about 1 cm, and increased to 2.85 ± 1.19 mm when the translation distance is about 3 cm. In the study on porcine data, the authors compared the tracked MVA to a manually segmented MVA. The overall accuracy is 2.37 ± 1.67 mm for single plane images and 2.35 ± 1.55 mm for biplane images. The interoperator variation in manual segmentation was 2.32 ± 1.24 mm for single plane images and 1.73 ± 1.18 mm for biplane images. The computational efficiency of the algorithm on a desktop computer with an Intel(®) Xeon(®) CPU @3.47 GHz and an NVIDIA GeForce 690 graphic card is such that the time required for registering four MVA points was about 60 ms.

CONCLUSIONS

The authors developed a rapid MVA tracking algorithm for use in the guidance of off-pump beating heart transapical mitral valve repair. This approach uses 2D biplane TEE images and was tested on a dynamic heart phantom and interventional porcine image data. Results regarding the accuracy and efficiency of the authors' MVA tracking algorithm are promising, and fulfill the requirements for surgical navigation.

Automatic mitral annulus tracking in volumetric ultrasound using non-rigid image registration

Analysis of mitral annular dynamics plays an important role in the diagnosis and selection of optimal valve repair strategies, but remains cumbersome and time-consuming if performed manually. In this paper we propose non-rigid image registration to automatically track the annulus in 3D ultrasound images for both normal and pathological valves, and compare the performance against manual tracing. Relevant clinical properties such as annular area, circumference and excursion could be extracted reliably by the tracking algorithm. The root-mean-square error, calculated as the difference between the manually traced landmarks (18 in total) and the automatic tracking, was 1.96 ± 0.46 mm over 10 valves (5 healthy and 5 diseased) which is within the clinically acceptable error range.

Semi-automated segmentation and quantification of mitral annulus and leaflets from transesophageal 3-D echocardiographic images

Quantification of three-dimensional (3-D) morphology of the mitral valve (MV) using real-time 3-D transesophageal echocardiography (RT3-D TEE) has proved to be a valuable tool for the assessment of MV pathologies, but of limited use in clinical practice because it relies on user-intensive approaches. This study presents a new algorithm for the segmentation and morphologic quantification of the mitral annulus (MA) and mitral leaflets (ML) in closed valve configuration from RT3-D TEE volumes. Following initialization, the MA and the ML and the coaptation line (CL) are automatically obtained in 3-D. Validation with manual tracings was performed on 33 patients, resulting in segmentation errors in the order of 0.7 mm and 0.6 mm for the MA and ML segmentation, in addition to good intra- and inter-observer reproducibility (coefficients of variation below 12% and 15%, respectively). The ability of the algorithm to assess different MV pathologies as well as repaired valves with implanted annular rings was also explored. The reported performance of the proposed fast, semi-automated MA and ML quantification makes it promising for future applications in clinical settings such as the operating room, where obtaining results in short time is important.

Fully automatic detection of salient features in 3-d transesophageal images

Most automated segmentation approaches to the mitral valve and left ventricle in 3-D echocardiography require a manual initialization. In this article, we propose a fully automatic scheme to initialize a multicavity segmentation approach in 3-D transesophageal echocardiography by detecting the left ventricle long axis, the mitral valve and the aortic valve location. Our approach uses a probabilistic and structural tissue classification to find structures such as the mitral and aortic valves; the Hough transform for circles to find the center of the left ventricle; and multidimensional dynamic programming to find the best position for the left ventricle long axis. For accuracy and agreement assessment, the proposed method was evaluated in 19 patients with respect to manual landmarks and as initialization of a multicavity segmentation approach for the left ventricle, the right ventricle, the left atrium, the right atrium and the aorta. The segmentation results revealed no statistically significant differences between manual and automated initialization in a paired t-test (p > 0.05). Additionally, small biases between manual and automated initialization were detected in the Bland-Altman analysis (bias, variance) for the left ventricle (-0.04, 0.10); right ventricle (-0.07, 0.18); left atrium (-0.01, 0.03); right atrium (-0.04, 0.13); and aorta (-0.05, 0.14). These results indicate that the proposed approach provides robust and accurate detection to initialize a multicavity segmentation approach without any user interaction.

Real-time aortic valve segmentation from transesophageal echocardiography sequence

PURPOSE

Geometric features of the aortic valve play an important role in many applications, such as the clinical diagnostics, shape modeling and image-guided cardiac interventions, especially for the transcatheter aortic valve implantation (TAVI) procedure. However, few works have been reported on the topic of aortic valve segmentation from transesophageal echocardiography (TEE) sequences. To obtain accurate segmentation results and further provide valid support for TAVI, this paper presents a real-time method for segmenting the aortic valve from intraoperative, short-axis view TEE sequences, using an improved probability estimation and continuous max-flow (CMF) approach.

METHODS

The proposed segmentation method includes two key stages: (1) In the probability estimation stage, five different prior frames spanning a cardiac circle are firstly selected with the aortic valve manually segmented by an expert. Then, the improved composite probability estimation (CPE) and single probability estimation (SPE) over the five prior frames are, respectively, constructed based on their radial average intensity and radial distance. (2) In the energy function construction stage, the similarity metric is calculated to find out the matching exponents between the current input TEE frame and the prior frames. The typical foreground and background intensities of prior images are therefore used to construct the corresponding energy function. Finally, the CMF approach, accelerated with a graphic processing unit (GPU), is employed to achieve the aortic valve contours in real time.

RESULTS

The evaluation study contained 30 sequences, with each containing 62-146 short-axis TEE frames. The results were compared with the manual segmentation (ground truth). The average symmetric contour distance (ASCD), dice metric (DM) and the reliability of the algorithm reached 0.85 ± 0.21 mm, 0.96 ± 0.01 and 0.90 (d = 0.95), respectively, and the computation time was 57.04 ± 8.98 ms per frame.

CONCLUSION

The experiment results reveal that the proposed method can achieve accurate and real-time segmentation of aortic valve from TEE sequence of short-axis view.

Automatic classification of intracardiac tumor and thrombi in echocardiography based on sparse representation

Identification of intracardiac masses in echocardiograms is one important task in cardiac disease diagnosis. To improve diagnosis accuracy, a novel fully automatic classification method based on the sparse representation is proposed to distinguish intracardiac tumor and thrombi in echocardiography. First, a region of interest is cropped to define the mass area. Then, a unique globally denoising method is employed to remove the speckle and preserve the anatomical structure. Subsequently, the contour of the mass and its connected atrial wall are described by the K-singular value decomposition and a modified active contour model. Finally, the motion, the boundary as well as the texture features are processed by a sparse representation classifier to distinguish two masses. Ninety-seven clinical echocardiogram sequences are collected to assess the effectiveness. Compared with other state-of-the-art classifiers, our proposed method demonstrates the best performance by achieving an accuracy of 96.91%, a sensitivity of 100%, and a specificity of 93.02%. It explicates that our method is capable of classifying intracardiac tumors and thrombi in echocardiography, potentially to assist the cardiologists in the clinical practice.

Fully automatic segmentation of the mitral leaflets in 3D transesophageal echocardiographic images using multi-atlas joint label fusion and deformable medial modeling

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 complementary probabilistic segmentation and shape modeling techniques (multi-atlas joint label fusion and deformable modeling with continuous medial representation) to automatically generate 3D geometric models of the mitral leaflets from 3D TEE image data. These models are unique in that they establish a shape-based coordinate system on the valves of different subjects and represent the leaflets volumetrically, as structures with locally varying thickness. In this work, expert image analysis is the gold standard for evaluating automatic segmentation. Without any user interaction, we demonstrate that the automatic segmentation method accurately captures patient-specific leaflet geometry at both systole and diastole in 3D TEE data acquired from a mixed population of subjects with normal valve morphology and mitral valve disease.

Using a shape prior for robust modeling of the mitral annulus on 4D ultrasound data

PURPOSE

Over 40,000 annuloplasty rings are implanted each year in the USA to treat mitral regurgitation. However, the used measuring techniques to select a suitable annuloplasty ring are imprecise and highly depending on the expert's experience. This can cause a re-occurrence of the mitral regurgitation or an annuloplasty ring dehiscence, and thus the necessity of a re-operation. We propose a method to create a 4D model of the mitral annulus from ultrasound data to enable precise measurement and patient-specific implant planning.

METHODS

An initial mitral annulus model is placed interactively in the 4D image data by defining commissure points and the annulus plane for one time step in diastole and systole. The model is automatically optimized using distinct image features. A shape and pose prior of the mitral annulus is used to compensate for artifacts and to enforce a plausible anatomical morphology, while a temporal alignment ensures a natural motion of the 4D model.

RESULTS

Ground truth data were created for 4D images of 42 patients with varying image quality. A parameter and shape prior training was performed on a third of the ground truth data, while the rest was used to validate the method. The average error of the resulting mitral annulus models was computed as 2.25 ( +/-0.38 ) mm. The average expert standard deviation was determined as 1.86 (+/-0.32 ) mm.

CONCLUSION

The proposed method enables the 4D modeling of mitral annuli based on ultrasound data in less than 2 min. The resulting models are comparable to manually delineated models and can be used for measurements of annular geometries and patient-specific annuloplasty treatment planning.

A novel aortic valve segmentation from ultrasound image using continuous max-flow approach

Geometric features of aortic valve can be applied in diagnostic, modeling and image-guided cardiac intervention, however methods to accurately and effectively delineate aortic valve from ultrasound (US) image are not well addressed. This paper proposes a novel aortic valve segmentation algorithm for intra-operative 2D short-axis US image using probability estimation and continuous max-flow (CMF) approach. The algorithm first calculates composite probability estimation (CPE) and single probability estimation (SPE) over 5 prior images based on both intensity and distance to the corresponding centroid, then the energy function for the current input image is constructed, followed by a Graphic Processing Unit (GPU) accelerated CMF approach to achieve aortic valve contours in approximately real time. Quantitative evaluations over 270 images acquired from 3 subjects indicated the results of the algorithm had good correlation with the manual segmentation results (ground truth) by an expert. The Average Symmetric Contour Distance (ASCD), Dice Metric (DM), and Reliability were employed to evaluate our algorithm, and the evaluation results of these three metrics were 1.79±0.46 (in pixels), 0.96±0.01 and 0.84 (d=0.95) respectively, where the computational time was 39.23±5.02 ms per frame.

Mitral valve closure prediction with 3-D personalized anatomical models and anisotropic hyperelastic tissue assumptions

This study is concerned with the development of patient-specific simulations of the mitral valve that use personalized anatomical models derived from 3-D transesophageal echocardiography (3-D TEE). The proposed method predicts the closed configuration of the mitral valve by solving for an equilibrium solution that balances various forces including blood pressure, tissue collision, valve tethering, and tissue elasticity. The model also incorporates realistic hyperelastic and anisotropic properties for the valve leaflets. This study compares hyperelastic tissue laws with a quasi-elastic law under various physiological parameters, and provides insights into error sensitivity to chordal placement, allowing for a preliminary comparison of the influence of the two factors (chords and models) on error. Predictive errors show the promise of the method, yielding aggregate median errors of the order of 1 mm, and computed strains and stresses show good correspondence with those reported in prior studies.

Patient-specific mitral valve closure prediction using 3D echocardiography

This article presents an approach to modeling the closure of the mitral valve using patient-specific anatomical information derived from 3D transesophageal echocardiography (TEE). Our approach uses physics-based modeling to solve for the stationary configuration of the closed valve structure from the patient-specific open valve structure, which is recovered using a user-in-the-loop, thin-tissue detector segmentation. The method uses a tensile shape-finding approach based on energy minimization. This method is employed to predict the aptitude of the mitral valve leaflets to coapt. We tested the method using 10 intraoperative 3D TEE sequences by comparing the closed valve configuration predicted from the segmented open valve with the segmented closed valve, taken as ground truth. Experiments show promising results, with prediction errors on par with 3D TEE resolution and with good potential for applications in pre-operative planning.

Segmentation of skin lesions in 2-D and 3-D ultrasound images using a spatially coherent generalized Rayleigh mixture model

This paper addresses the problem of jointly estimating the statistical distribution and segmenting lesions in multiple-tissue high-frequency skin ultrasound images. The distribution of multiple-tissue images is modeled as a spatially coherent finite mixture of heavy-tailed Rayleigh distributions. Spatial coherence inherent to biological tissues is modeled by enforcing local dependence between the mixture components. An original Bayesian algorithm combined with a Markov chain Monte Carlo method is then proposed to jointly estimate the mixture parameters and a label-vector associating each voxel to a tissue. More precisely, a hybrid Metropolis-within-Gibbs sampler is used to draw samples that are asymptotically distributed according to the posterior distribution of the Bayesian model. The Bayesian estimators of the model parameters are then computed from the generated samples. Simulation results are conducted on synthetic data to illustrate the performance of the proposed estimation strategy. The method is then successfully applied to the segmentation of in vivo skin tumors in high-frequency 2-D and 3-D ultrasound images.

Development of a semi-automated method for mitral valve modeling with medial axis representation using 3D ultrasound

PURPOSE

Precise 3D modeling of the mitral valve has the potential to improve our understanding of valve morphology, particularly in the setting of mitral regurgitation (MR). Toward this goal, the authors have developed a user-initialized algorithm for reconstructing valve geometry from transesophageal 3D ultrasound (3D US) image data.

METHODS

Semi-automated image analysis was performed on transesophageal 3D US images obtained from 14 subjects with MR ranging from trace to severe. Image analysis of the mitral valve at midsystole had two stages: user-initialized segmentation and 3D deformable modeling with continuous medial representation (cm-rep). Semi-automated segmentation began with user-identification of valve location in 2D projection images generated from 3D US data. The mitral leaflets were then automatically segmented in 3D using the level set method. Second, a bileaflet deformable medial model was fitted to the binary valve segmentation by Bayesian optimization. The resulting cm-rep provided a visual reconstruction of the mitral valve, from which localized measurements of valve morphology were automatically derived. The features extracted from the fitted cm-rep included annular area, annular circumference, annular height, intercommissural width, septolateral length, total tenting volume, and percent anterior tenting volume. These measurements were compared to those obtained by expert manual tracing. Regurgitant orifice area (ROA) measurements were compared to qualitative assessments of MR severity. The accuracy of valve shape representation with cm-rep was evaluated in terms of the Dice overlap between the fitted cm-rep and its target segmentation.

RESULTS

The morphological features and anatomic ROA derived from semi-automated image analysis were consistent with manual tracing of 3D US image data and with qualitative assessments of MR severity made on clinical radiology. The fitted cm-reps accurately captured valve shape and demonstrated patient-specific differences in valve morphology among subjects with varying degrees of MR severity. Minimal variation in the Dice overlap and morphological measurements was observed when different cm-rep templates were used to initialize model fitting.

CONCLUSIONS

This study demonstrates the use of deformable medial modeling for semi-automated 3D reconstruction of mitral valve geometry using transesophageal 3D US. The proposed algorithm provides a parametric geometrical representation of the mitral leaflets, which can be used to evaluate valve morphology in clinical ultrasound images.

Pathology of myxomatous mitral valve disease in the dog

Mitral valve competence requires complex interplay between structures that comprise the mitral apparatus - the mitral annulus, mitral valve leaflets, chordae tendineae, papillary muscles, and left atrial and left ventricular myocardium. Myxomatous mitral valve degeneration is prevalent in the canine, and most adult dogs develop some degree of mitral valve disease as they age, highlighting the apparent vulnerability of canine heart valves to injury. Myxomatous valvular remodeling is associated with characteristic histopathologic features. Changes include expansion of extracellular matrix with glycosaminoglycans and proteoglycans; valvular interstitial cell alteration; and attenuation or loss of the collagen-laden fibrosa layer. These lead to malformation of the mitral apparatus, biomechanical dysfunction, and mitral incompetence. Mitral regurgitation is the most common manifestation of myxomatous valve disease and in advanced stages, associated volume overload promotes progressive valvular regurgitation, left atrial and left ventricular remodeling, atrial tears, chordal rupture, and congestive heart failure. Future studies are necessary to identify clinical-pathologic correlates that track disease severity and progression, detect valve dysfunction, and facilitate risk stratification. It remains unresolved whether, or to what extent, the pathobiology of myxomatous mitral valve degeneration is the same between breeds of dogs, between canines and humans, and how these features are related to aging and genetics.

On the Design of an Interactive, Patient-Specific Surgical Simulator for Mitral Valve Repair

Surgical repair of the mitral valve is a difficult procedure that is often avoided in favor of less effective valve replacement because of the associated technical challenges facing non-expert surgeons. In the interest of increasing the rate of valve repair, an accurate, interactive surgical simulator for mitral valve repair was developed. With a haptic interface, users can interact with a mechanical model during simulation to aid in the development of a surgical plan and then virtually implement the procedure to assess its efficacy. Sub-millimeter accuracy was achieved in a validation study, and the system was successfully used by a cardiac surgeon to repair three virtual pathological valves.

Mitral annulus segmentation from four-dimensional ultrasound using a valve state predictor and constrained optical flow

Measurement of the shape and motion of the mitral valve annulus has proven useful in a number of applications, including pathology diagnosis and mitral valve modeling. Current methods to delineate the annulus from four-dimensional (4D) ultrasound, however, either require extensive overhead or user-interaction, become inaccurate as they accumulate tracking error, or they do not account for annular shape or motion. This paper presents a new 4D annulus segmentation method to account for these deficiencies. The method builds on a previously published three-dimensional (3D) annulus segmentation algorithm that accurately and robustly segments the mitral annulus in a frame with a closed valve. In the 4D method, a valve state predictor determines when the valve is closed. Subsequently, the 3D annulus segmentation algorithm finds the annulus in those frames. For frames with an open valve, a constrained optical flow algorithm is used to the track the annulus. The only inputs to the algorithm are the selection of one frame with a closed valve and one user-specified point near the valve, neither of which needs to be precise. The accuracy of the tracking method is shown by comparing the tracking results to manual segmentations made by a group of experts, where an average RMS difference of 1.67±0.63mm was found across 30 tracked frames.

Real-time image-based rigid registration of three-dimensional ultrasound

Registration of three-dimensional ultrasound (3DUS) volumes is necessary in several applications, such as when stitching volumes to expand the field of view or when stabilizing a temporal sequence of volumes to cancel out motion of the probe or anatomy. Current systems that register 3DUS volumes either use external tracking systems (electromagnetic or optical), which add expense and impose limitations on acquisitions, or are image-based methods that operate offline and are incapable of providing immediate feedback to clinicians. This paper presents a real-time image-based algorithm for rigid registration of 3DUS volumes designed for acquisitions in which small probe displacements occur between frames. Described is a method for feature detection and descriptor formation that takes into account the characteristics of 3DUS imaging. Volumes are registered by determining a correspondence between these features. A global set of features is maintained and integrated into the registration, which limits the accumulation of registration error. The system operates in real-time (i.e. volumes are registered as fast or faster than they are acquired) by using an accelerated framework on a graphics processing unit. The algorithm's parameter selection and performance is analyzed and validated in studies which use both water tank and clinical images. The resulting registration accuracy is comparable to similar feature-based registration methods, but in contrast to these methods, can register 3DUS volumes in real-time.

A computer-aided method for automatic localization and thickness measurement of peritoneum in ultrasound images

This paper presents a method of automatically measuring peritoneum thickness in ultrasound images. In our previous work, a method of manually selecting the region of interest (ROI) area has been developed. To achieve an automatic ROI area selection, two phases: Gaussian high-pass filtering and bilateral filtering, are used in the proposed method. In the bilateral filtering phase, the ultrasound image is enhanced for obtaining more details of the peritoneum so that probable areas can be extracted. In the other phase, the ultrasound image is processed with a Gaussian high pass filter, and the result is used to locate the precise area of peritoneum in the first phase result. The experimental results show that the proposed method has high accuracy and fast processing speed in determining the peritoneum area and its thickness distribution.

Patient-specific mitral leaflet segmentation from 4D ultrasound

Segmenting the mitral valve during closure and throughout a cardiac cycle from four dimensional ultrasound (4DUS) is important for creation and validation of mechanical models and for improved visualization and understanding of mitral valve behavior. Current methods of segmenting the valve from 4DUS either require extensive user interaction and initialization, do not maintain the valve geometry across a cardiac cycle, or are incapable of producing a detailed coaptation line and surface. We present a method of segmenting the mitral valve annulus and leaflets from 4DUS such that a detailed, patient-specific annulus and leaflets are tracked throughout mitral valve closure, resulting in a detailed coaptation region. The method requires only the selection of two frames from a sequence indicating the start and end of valve closure and a single point near a closed valve. The annulus and leaflets are first found through direct segmentation in the appropriate frames and then by tracking the known geometry to the remaining frames. We compared the automatically segmented meshes to expert manual tracings for both a normal and diseased mitral valve, and found an average difference of 0.59 +/- 0.49 mm, which is on the order of the spatial resolution of the ultrasound volumes (0.5-1.0 mm/voxel).