Automatic model-based segmentation of the heart in CT images

Automatic multi-model-based segmentation of the left atrium in cardiac MRI scans

Model-based segmentation approaches have been proven to produce very accurate segmentation results while simultaneously providing an anatomic labeling for the segmented structures. However, variations of the anatomy, as they are often encountered e.g. on the drainage pattern of the pulmonary veins to the left atrium, cannot be represented by a single model. Automatic model selection extends the model-based segmentation approach to handling significant variational anatomies without user interaction. Using models for the three most common anatomical variations of the left atrium, we propose a method that uses an estimation of the local fit of different models to select the best fitting model automatically. Our approach employs the support vector machine for the automatic model selection. The method was evaluated on 42 very accurate segmentations of MRI scans using three different models. The correct model was chosen in 88.1% of the cases. In a second experiment, reflecting average segmentation results, the model corresponding to the clinical classification was automatically found in 78.0% of the cases.

Limited angle C-arm tomography and segmentation for guidance of atrial fibrillation ablation procedures

Angiographic projections of the left atrium (LA) and the pulmonary veins (PV) acquired with a rotational C-arm system are used for 3D image reconstruction and subsequent automatic segmentation of the LA and PV to be used as roadmap in fluoroscopy guided LA ablation procedures. Acquisition of projections at high oblique angulations may be problematic due to increased collision danger of the detector with the right shoulder of the patient. We investigate the accuracy of image reconstruction and model based roadmap segmentation using limited angle C-arm tomography. The reduction of the angular range from 200 degrees to 150 degrees leads only to a moderate increase of the segmentation error from 1.5 mm to 2.0 mm if matched conditions are used in the segmentation, i.e., the model based segmentation is trained on images reconstructed with the same angular range as the test images. The minor decrease in accuracy may be outweighed by clinical workflow improvement, gained when large C-arm angulations can be avoided.

Personalization of atrial anatomy and electrophysiology as a basis for clinical modeling of radio-frequency ablation of atrial fibrillation

Multiscale cardiac modeling has made great advances over the last decade. Highly detailed atrial models were created and used for the investigation of initiation and perpetuation of atrial fibrillation. The next challenge is the use of personalized atrial models in clinical practice. In this study, a framework of simple and robust tools is presented, which enables the generation and validation of patient-specific anatomical and electrophysiological atrial models. Introduction of rule-based atrial fiber orientation produced a realistic excitation sequence and a better correlation to the measured electrocardiograms. Personalization of the global conduction velocity lead to a precise match of the measured P-wave duration. The use of a virtual cohort of nine patient and volunteer models averaged out possible model-specific errors. Intra-atrial excitation conduction was personalized manually from left atrial local activation time maps. Inclusion of LE-MRI data into the simulations revealed possible gaps in ablation lesions. A fast marching level set approach to compute atrial depolarization was extended to incorporate anisotropy and conduction velocity heterogeneities and reproduced the monodomain solution. The presented chain of tools is an important step towards the use of atrial models for the patient-specific AF diagnosis and ablation therapy planing.

Fusion of ultrasound B-mode and vibro-elastography images for automatic 3D segmentation of the prostate

Prostate segmentation in B-mode images is a challenging task even when done manually by experts. In this paper we propose a 3D automatic prostate segmentation algorithm which makes use of information from both ultrasound B-mode and vibro-elastography data.We exploit the high contrast to noise ratio of vibro-elastography images of the prostate, in addition to the commonly used B-mode images, to implement a 2D Active Shape Model (ASM)-based segmentation algorithm on the midgland image. The prostate model is deformed by a combination of two measures: the gray level similarity and the continuity of the prostate edge in both image types. The automatically obtained mid-gland contour is then used to initialize a 3D segmentation algorithm which models the prostate as a tapered and warped ellipsoid. Vibro-elastography images are used in addition to ultrasound images to improve boundary detection.We report a Dice similarity coefficient of 0.87±0.07 and 0.87±0.08 comparing the 2D automatic contours with manual contours of two observers on 61 images. For 11 cases, a whole gland volume error of 10.2±2.2% and 13.5±4.1% and whole gland volume difference of -7.2±9.1% and -13.3±12.6% between 3D automatic and manual surfaces of two observers is obtained. This is the first validated work showing the fusion of B-mode and vibro-elastography data for automatic 3D segmentation of the prostate.

Towards personalized clinical in-silico modeling of atrial anatomy and electrophysiology

Computational atrial models aid the understanding of pathological mechanisms and therapeutic measures in basic research. The use of biophysical models in a clinical environment requires methods to personalize the anatomy and electrophysiology (EP). Strategies for the automation of model generation and for evaluation are needed. In this manuscript, the current efforts of clinical atrial modeling in the euHeart project are summarized within the context of recent publications in this field. Model-based segmentation methods allow for the automatic generation of ready-to-simulate patient-specific anatomical models. EP models can be adapted to patient groups based on a-priori knowledge and to the individual without significant further data acquisition. ECG and intracardiac data build the basis for excitation personalization. Information from late enhancement (LE) MRI can be used to evaluate the success of radio-frequency ablation (RFA) procedures and interactive virtual atria pave the way for RFA planning. Atrial modeling is currently in a transition from the sole use in basic research to future clinical applications. The proposed methods build the framework for model-based diagnosis and therapy evaluation and planning. Complex models allow to understand biophysical mechanisms and enable the development of simplified models for clinical applications.

Automatic renal cortex segmentation using implicit shape registration and novel multiple surfaces graph search

In this paper, we present an automatic renal cortex segmentation approach using the implicit shape registration and novel multiple surfaces graph search. The proposed approach is based on a hierarchy system. First, the whole kidney is roughly initialized using an implicit shape registration method, with the shapes embedded in the space of Euclidean distance functions. Second, the outer and inner surfaces of renal cortex are extracted utilizing multiple surfaces graph searching, which is extended to allow for varying sampling distances and physical constraints to better separate the renal cortex and renal column. Third, a renal cortex refining procedure is applied to detect and reduce incorrect segmentation pixels around the renal pelvis, further improving the segmentation accuracy. The method was evaluated on 17 clinical computed tomography scans using the leave-one-out strategy with five metrics: Dice similarity coefficient (DSC), volumetric overlap error (OE), signed relative volume difference (SVD), average symmetric surface distance (D(avg)), and average symmetric rms surface distance (D(rms)). The experimental results of DSC, OE, SVD, D(avg) , and D(rms) were 90.50% ± 1.19%, 4.38% ± 3.93%, 2.37% ± 1.72%, 0.14 mm ± 0.09 mm , and 0.80 mm ± 0.64 mm, respectively. The results showed the feasibility, efficiency, and robustness of the proposed method.

A game-theoretic framework for landmark-based image segmentation

A novel game-theoretic framework for landmark-based image segmentation is presented. Landmark detection is formulated as a game, in which landmarks are players, landmark candidate points are strategies, and likelihoods that candidate points represent landmarks are payoffs, determined according to the similarity of image intensities and spatial relationships between the candidate points in the target image and their corresponding landmarks in images from the training set. The solution of the formulated game-theoretic problem is the equilibrium of candidate points that represent landmarks in the target image and is obtained by a novel iterative scheme that solves the segmentation problem in polynomial time. The object boundaries are finally extracted by applying dynamic programming to the optimal path searching problem between the obtained adjacent landmarks. The performance of the proposed framework was evaluated for segmentation of lung fields from chest radiographs and heart ventricles from cardiac magnetic resonance cross sections. The comparison to other landmark-based segmentation techniques shows that the results obtained by the proposed game-theoretic framework are highly accurate and precise in terms of mean boundary distance and area overlap. Moreover, the framework overcomes several shortcomings of the existing techniques, such as sensitivity to initialization and convergence to local optima.

Pulmonary artery and right ventricle assessment in pulmonary hypertension: correlation between functional parameters of ECG-gated CT and right-side heart catheterization

BACKGROUND

Right ventricular function predicts outcome in patients with pulmonary hypertension (PH). Therefore accurate assessment of right ventricular function is essential to graduate severity, assess follow-up, and response to therapy.

PURPOSE

To evaluate whether PH severity could be assessed using electrocardiography-gated CT (ECG-gated CT) functional parameters. A further objective was to evaluate cardiac output (CO) using two ECG-gated CT methods: the reference Simpson technique and the fully automatic technique generated by commercially available cardiac software.

MATERIAL AND METHODS

Our institutional review board approved this study; patient consent was not required. Twenty-seven patients who had undergone ECG-gated CT and right heart catheterization (RHC) were included. Two independent observers measured pulmonary artery (PA) diameter, PA distensibility, aorta diameter, right ventricular cardiac output (CT-RVCO) and right ventricular ejection fraction (CT-RVEF) with automatic and Simpson techniques on ECG-gated CT. RHC-CO and mean pulmonary arterial pressure (mPAP) were measured on RHC. Relationship between ECG-gated CT and RHC measurements was tested with linear regression analysis.

RESULTS

Inter-observer agreement was good for all measurements (r > 0.7) except for CT-RVCO calculated with Simpson's technique (r = 0.63). Pulmonary artery (PA) distensibility was significantly correlated to mPAP (r = -0.426, P = 0.027). CT-RVEF was correlated with mPAP only when issued from Simpson technique (r = -0.417, P = 0.034). CT-RVEF was not significantly correlated to RHC-CO (P > 0.2). CT-RVCO measured with Simpson technique (r = 0.487, P = 0.010) and automatic segmentation (r = 0.549, P = 0.005) correlated equally with RHC-CO.

CONCLUSION

CT-RVEF and CT-RVCO measured on ECG-gated CT are significantly correlated, respectively, to mPAP and RHC-CO in this population with severe reduction of the right ventricular ejection fraction and could be useful for evaluating and following patients with PH.

Bridging the text-image gap: a decision support tool for real-time PACS browsing

In this paper, we introduce an ontology-based technology that bridges the gap between MR images on the one hand and knowledge sources on the other hand. The proposed technology allows the user to express interest in a body region by selecting this region on the MR image he or she is viewing with a mouse device. The proposed technology infers the intended body structure from the manual selection and searches the external knowledge source for pertinent information. This technology can be used to bridge the gap between image data in the clinical workflow and (external) knowledge sources that help to assess the case with increased certainty, accuracy, and efficiency. We evaluate an instance of the proposed technology in the neurodomain by means of a user study in which three neuroradiologists participated. The user study shows that the technology has high recall (>95%) when it comes to inferring the intended brain region from the participant's manual selection. We are confident that this helps to increase the experience of browsing external knowledge sources.

A framework for personalization of computational models of the human atria

A framework for step-by-step personalization of a computational model of human atria is presented. Beginning with anatomical modeling based on CT or MRI data, next fiber structure is superimposed using a rule-based method. If available, late-enhancement-MRI images can be considered in order to mark fibrotic tissue. A first estimate of individual electrophysiology is gained from BSPM data solving the inverse problem of ECG. A final adjustment of electrophysiology is realized using intracardiac measurements. The framework is applied using several patient data. First clinical application will be computer assisted planning of RF-ablation for treatment of atrial flutter and atrial fibrillation.

Computational modeling of the human atrial anatomy and electrophysiology

This review article gives a comprehensive survey of the progress made in computational modeling of the human atria during the last 10 years. Modeling the anatomy has emerged from simple "peanut"-like structures to very detailed models including atrial wall and fiber direction. Electrophysiological models started with just two cellular models in 1998. Today, five models exist considering e.g. details of intracellular compartments and atrial heterogeneity. On the pathological side, modeling atrial remodeling and fibrotic tissue are the other important aspects. The bridge to data that are measured in the catheter laboratory and on the body surface (ECG) is under construction. Every measurement can be used either for model personalization or for validation. Potential clinical applications are briefly outlined and future research perspectives are suggested.

Centerline extraction with principal curve tracing to improve 3D level set esophagus segmentation in CT images

For radiotherapy planning, contouring of target volume and healthy structures at risk in CT volumes is essential. To automate this process, one of the available segmentation techniques can be used for many thoracic organs except the esophagus, which is very hard to segment due to low contrast. In this work we propose to initialize our previously introduced model based 3D level set esophagus segmentation method with a principal curve tracing (PCT) algorithm, which we adapted to solve the esophagus centerline detection problem. To address challenges due to low intensity contrast, we enhanced the PCT algorithm by learning spatial and intensity priors from a small set of annotated CT volumes. To locate the esophageal wall, the model based 3D level set algorithm including a shape model that represents the variance of esophagus wall around the estimated centerline is utilized. Our results show improvement in esophagus segmentation when initialized by PCT compared to our previous work, where an ad hoc centerline initialization was performed. Unlike previous approaches, this work does not need a very large set of annotated training images and has similar performance.

Regression-based cardiac motion prediction from single-phase CTA

State of the art cardiac computed tomography (CT) enables the acquisition of imaging data of the heart over the entire cardiac cycle at concurrent high spatial and temporal resolution. However, in clinical practice, acquisition is increasingly limited to 3-D images. Estimating the shape of the cardiac structures throughout the entire cardiac cycle from a 3-D image is therefore useful in applications such as the alignment of preoperative computed tomography angiography (CTA) to intra-operative X-ray images for improved guidance in coronary interventions. We hypothesize that the motion of the heart is partially explained by its shape and therefore investigate the use of three regression methods for motion estimation from single-phase shape information. Quantitative evaluation on 150 4-D CTA images showed a small, but statistically significant, increase in the accuracy of the predicted shape sequences when using any of the regression methods, compared to shape-independent motion prediction by application of the mean motion. The best results were achieved using principal component regression resulting in point-to-point errors of 2.3±0.5 mm, compared to values of 2.7±0.6 mm for shape-independent motion estimation. Finally, we showed that this significant difference withstands small variations in important parameter settings of the landmarking procedure.

A new automatic algorithm to extract craniofacial measurements from fetal three-dimensional volumes

OBJECTIVES

Three-dimensional (3D) ultrasound is useful in the prenatal evaluation of fetal craniofacial structures, particularly as it provides a multiplanar view. However, an expert must designate the area of interest and the appropriate view, making measurement of fetal structures using 3D ultrasound both time-consuming and subjective. In this study we propose an image analysis system that measures automatically and precisely the fetal craniofacial structures and evaluate its performance in the second trimester of pregnancy using a new 3D volume analysis algorithm.

METHODS

A universal facial surface template model containing the geometric shape information of a fetal craniofacial structure was constructed from a fetal phantom. Using the proposed image analysis system we fitted this stored template model using a model deformation approach to individual fetal 3D facial volumes from 11 mid-trimester fetuses, and extracted automatically the following standard measurements: biparietal diameter (BPD), occipitofrontal diameter (OFD), interorbital diameter (IOD), bilateral orbital diameter (BOD) and distance between vertex and nasion (VN). The same five parameters were measured manually by an expert and the results compared.

RESULTS

Comparison of the algorithm-based automatic measurements with manual measurements made by an expert gave correlation coefficients of 0.99 for BPD, 0.98 for OFD, 0.80 for BOD, 0.83 for IOD and 0.99 for VN. There were no significant differences between automatic and manual measurements.

CONCLUSION

Our proposed system measures precisely the fetal craniofacial structures using 3D ultrasound, making it potentially useful for clinical service. This system could also be applied to other clinical fields in future testing.

Multi-compartment heart segmentation in CT angiography using a spatially varying gaussian classifier

OBJECTIVE

A fully automated and efficient method for segmenting ten major structures within the heart in Cardiac CT Angiography data for the purposes of display or cardiac functional analysis.

MATERIALS AND METHODS

A spatially varying Gaussian classifier is a flexible model for segmentation, combining the advantages of atlas-based frameworks, with supervised intensity models. It is composed of an independent Gaussian classifier at each voxel and uses non-rigid registration for the initial spatial alignment. We show how this large model can be trained efficiently and present a novel smoothing technique based on normalised convolution to mitigate inherent overfitting issues. The 30 datasets used in this study are selected from a variety of different scanners in order to test the robustness and stability of the algorithm. The datasets were manually segmented by a trained clinician.

RESULTS

The method was evaluated in a leave-one-out fashion, and the results were compared to other state of the art methods in the field, with a mean surface-to-surface distance of between 0.61 and 2.12 mm for different compartments.

CONCLUSION

The accuracy of this method is comparable to other state of the art methods in the field. Its benefits lie in its conceptual simplicity and its general applicability. Only one non-rigid registration is required, giving it a speed advantage over multi-atlas approaches. Further accuracy may be achievable through the incorporation of an explicit shape model.

Automatic model-based contour detection of left ventricle myocardium from cardiac CT images

PURPOSE

For accurate evaluation of myocardial perfusion on computed tomography images, precise identification of the myocardial borders of the left ventricle (LV) is mandatory. In this article, we propose a method to detect the contour of LV myocardium automatically and accurately.

METHODS

Our detection method is based on active shape model. For precise detection, we estimate the pose and shape parameters separately by three steps: LV coordinate system estimation, myocardial shape estimation, and transformation. In LV coordinate system estimation, we detect heart features followed by the entire LV by introducing machine-learning approach. Since the combination of two types feature detection covers the LV variation, such as pose or shape, we can estimate the LV coordinate system robustly. In myocardial shape estimation, we minimize the energy function including pattern error around myocardium with adjustment of pattern model to input image using estimated concentration of contrast dye. Finally, we detect LV myocardial contours in the input images by transforming the estimated myocardial shape using the matrix composed of the vectors calculated by the LV coordinate system estimation.

RESULTS

In our experiments with 211 images from 145 patients, mean myocardial contours point-to-point errors for our method as compared to ground truth were 1.02 mm for LV endocardium and 1.07 mm for LV epicardium. The average computation time was 2.4 s (on a 3.46 GHz processor with 2-multithreading process).

CONCLUSIONS

Our method achieved accurate and fast myocardial contour detection which may be sufficient for myocardial perfusion examination.

Fully automatic geometry planning for cardiac MR imaging and reproducibility of functional cardiac parameters

PURPOSE

To establish operator-independent, fully automated planning of standard cardiac geometries and to determine the impact on interstudy reproducibility of cardiac functional parameters.

MATERIALS AND METHODS

Cardiac MR imaging was done in 50 patients referred for left-ventricular function assessment. In all patients, first standard manual planning was performed followed by automatic planning (AUTO1) and repeat automatic planning (AUTO2) after repositioning the patient to investigate interstudy reproducibility. Cardiac functional parameters were assessed and cine scans were visually graded on a 4-point scale from nondiagnostic to excellent.

RESULTS

Overall success rate of AUTO was 94% with good to excellent geometry planning in >94% of cine standard views. Comparing manual versus fully automated planning, a high agreement of cardiac functional parameters (Lin's concordance correlation coefficient, 0.91 to 0.99) with minimal percent bias (0.24 to 3.84%) was found. In addition, a high interstudy reproducibility of automatic planning was demonstrated (Lin's concordance correlation coefficient, 0.89 to 0.99; percent bias, 0.38 to 5.04%; precision, 3.46 to 9.09%).

CONCLUSION

Fully automated planning of cardiac geometries could reliably be performed in patients showing a variety of cardiovascular pathologies. Standard cardiac geometries were precisely replicated and functional parameters were highly accurate.

Manifold learning for shape guided segmentation of cardiac boundaries: application to 3D+t cardiac MRI

In this paper we propose a new method for shape guided segmentation of cardiac boundaries based on manifold learning of the shapes represented by the phase field approximation of the Mumford-Shah functional. A novel distance is defined to measure the similarity of shapes without requiring deformable registration. Cardiac motion is compensated and phases are mapped into one reference phase, that is the end of diastole, to avoid time warping and synchronization at all cardiac phases. Non-linear embedding of these 3D shapes extracts the manifold of the inter-subject variation of the heart shape to be used for guiding the segmentation for a new subject. For validation the method is applied to a comprehensive dataset of 3D+t cardiac Cine MRI from normal subjects and patients.

Similarity enhancement for automatic segmentation of cardiac structures in computed tomography volumes

The aim of this research is proposing a 3-D similarity enhancement technique useful for improving the segmentation of cardiac structures in Multi-Slice Computerized Tomography (MSCT) volumes. The similarity enhancement is obtained by subtracting the intensity of the current voxel and the gray levels of their adjacent voxels in two volumes resulting after preprocessing. Such volumes are: a. - a volume obtained after applying a Gaussian distribution and a morphological top-hat filter to the input and b. - a smoothed volume generated by processing the input with an average filter. Then, the similarity volume is used as input to a region growing algorithm. This algorithm is applied to extract the shape of cardiac structures, such as left and right ventricles, in MSCT volumes. Qualitative and quantitative results show the good performance of the proposed approach for discrimination of cardiac cavities.

Modeling and Representation of Human Hearts for Volumetric Measurement

This paper investigates automatic construction of a three-dimensional heart model from a set of medical images, represents it in a deformable shape, and uses it to perform volumetric measurements. This not only significantly improves its reliability and accuracy but also makes it possible to derive valuable novel information, like various assessment and dynamic volumetric measurements. The method is based on a flexible model trained from hundreds of patient image sets by a genetic algorithm, which takes advantage of complete segmentation of the heart shape to form a geometrical heart model. For an image set of a new patient, an interpretation scheme is used to obtain its shape and evaluate some important parameters. Apart from automatic evaluation of traditional heart functions, some new information of cardiovascular diseases may be recognized from the volumetric analysis.

Automatic Alignment of Myocardial Perfusion Images With Contrast-Enhanced Cardiac Computed Tomography

Explicit fusion of perfusion data from Positron Emission Tomography (PET) or Single Photon Emission Computed Tomography (SPECT) with coronary artery anatomy from Computed Tomographic Coronary Angiography (CTA) has been shown to improve the diagnostic yield for coronary artery disease (CAD) compared to either modality alone. However, most clinically available methods were developed for multimodal scanners or require interactive alignment prior to display and analysis. A new approach was developed to register the two distributions obtained either from a single multimodal imager or from separate scanners, and a preliminary validation was undertaken to compare the automatic alignment to interactive alignment by two experts.

Clinical evaluation of respiratory motion compensation for anatomical roadmap guided cardiac electrophysiology procedures

X-ray fluoroscopically guided cardiac electrophysiological procedures are routinely carried out for diagnosis and treatment of cardiac arrhythmias. X-ray images have poor soft tissue contrast and, for this reason, overlay of static 3-D roadmaps derived from preprocedural volumetric data can be used to add anatomical information. However, the registration between the 3-D roadmap and the 2-D X-ray image can be compromised by patient respiratory motion. Three methods were designed and evaluated to correct for respiratory motion using features in the 2-D X-ray images. The first method is based on tracking either the diaphragm or the heart border using the image intensity in a region of interest. The second method detects the tracheal bifurcation using the generalized Hough transform and a 3-D model derived from 3-D preoperative volumetric data. The third method is based on tracking the coronary sinus (CS) catheter. This method uses blob detection to find all possible catheter electrodes in the X-ray image. A cost function is applied to select one CS catheter from all catheter-like objects. All three methods were applied to X-ray images from 18 patients undergoing radiofrequency ablation for the treatment of atrial fibrillation. The 2-D target registration errors (TRE) at the pulmonary veins were calculated to validate the methods. A TRE of 1.6 mm ± 0.8 mm was achieved for the diaphragm tracking; 1.7 mm ± 0.9 mm for heart border tracking, 1.9 mm ± 1.0 mm for trachea tracking, and 1.8 mm ± 0.9 mm for CS catheter tracking. We present a comprehensive comparison between the techniques in terms of robustness, as computed by tracking errors, and accuracy, as computed by TRE using two independent approaches.

Patient-specific electromechanical models of the heart for the prediction of pacing acute effects in CRT: a preliminary clinical validation

Cardiac resynchronisation therapy (CRT) is an effective treatment for patients with congestive heart failure and a wide QRS complex. However, up to 30% of patients are non-responders to therapy in terms of exercise capacity or left ventricular reverse remodelling. A number of controversies still remain surrounding patient selection, targeted lead implantation and optimisation of this important treatment. The development of biophysical models to predict the response to CRT represents a potential strategy to address these issues. In this article, we present how the personalisation of an electromechanical model of the myocardium can predict the acute haemodynamic changes associated with CRT. In order to introduce such an approach as a clinical application, we needed to design models that can be individualised from images and electrophysiological mapping of the left ventricle. In this paper the personalisation of the anatomy, the electrophysiology, the kinematics and the mechanics are described. The acute effects of pacing on pressure development were predicted with the in silico model for several pacing conditions on two patients, achieving good agreement with invasive haemodynamic measurements: the mean error on dP/dt(max) is 47.5±35mmHgs(-1), less than 5% error. These promising results demonstrate the potential of physiological models personalised from images and electrophysiology signals to improve patient selection and plan CRT.

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

Recently, model-based methods for the automatic segmentation of the heart chambers have been proposed. An important application of these methods is the characterization of the heart function. Heart models are, however, increasingly used for interventional guidance making it necessary to also extract the attached great vessels. It is, for instance, important to extract the left atrium and the proximal part of the pulmonary veins to support guidance of ablation procedures for atrial fibrillation treatment. For cardiac resynchronization therapy, a heart model including the coronary sinus is needed. We present a heart model comprising the four heart chambers and the attached great vessels. By assigning individual linear transformations to the heart chambers and to short tubular segments building the great vessels, variable sizes of the heart chambers and bending of the vessels can be described in a consistent way. A configurable algorithmic framework that we call adaptation engine matches the heart model automatically to cardiac CT angiography images in a multi-stage process. First, the heart is detected using a Generalized Hough Transformation. Subsequently, the heart chambers are adapted. This stage uses parametric as well as deformable mesh adaptation techniques. In the final stage, segments of the large vascular structures are successively activated and adapted. To optimize the computational performance, the adaptation engine can vary the mesh resolution and freeze already adapted mesh parts. The data used for validation were independent from the data used for model-building. Ground truth segmentations were generated for 37 CT data sets reconstructed at several cardiac phases from 17 patients. Segmentation errors were assessed for anatomical sub-structures resulting in a mean surface-to-surface error ranging 0.50-0.82mm for the heart chambers and 0.60-1.32mm for the parts of the great vessels visible in the images.

Evaluation of a multi-atlas based method for segmentation of cardiac CTA data: a large-scale, multicenter, and multivendor study

PURPOSE

Computed tomography angiography (CTA) is increasingly used for the diagnosis of coronary artery disease (CAD). However, CTA is not commonly used for the assessment of ventricular and atrial function, although functional information extracted from CTA data is expected to improve the diagnostic value of the examination. In clinical practice, the extraction of ventricular and atrial functional information, such as stroke volume and ejection fraction, requires accurate delineation of cardiac chambers. In this paper, we investigated the accuracy and robustness of cardiac chamber delineation using a multiatlas based segmentation method on multicenter and multivendor CTA data.

METHODS

A fully automatic multiatlas based method for segmenting the whole heart (i.e., the outer surface of the pericardium) and cardiac chambers from CTA data is presented and evaluated. In the segmentation approach, eight atlas images are registered to a new patient's CTA scan. The eight corresponding manually labeled images are then propagated and combined using a per voxel majority voting procedure, to obtain a cardiac segmentation.

RESULTS

The method was evaluated on a multicenter/multivendor database, consisting of (1) a set of 1380 Siemens scans from 795 patients and (2) a set of 60 multivendor scans (Siemens, Philips, and GE) from different patients, acquired in six different institutions worldwide. A leave-one-out 3D quantitative validation was carried out on the eight atlas images; we obtained a mean surface-to-surface error of 0.94 +/- 1.12 mm and an average Dice coefficient of 0.93 was achieved. A 2D quantitative evaluation was performed on the 60 multivendor data sets. Here, we observed a mean surface-to-surface error of 1.26 +/- 1.25 mm and an average Dice coefficient of 0.91 was achieved. In addition to this quantitative evaluation, a large-scale 2D and 3D qualitative evaluation was performed on 1380 and 140 images, respectively. Experts evaluated that 49% of the 1380 images were very accurately segmented (below 1 mm error) and that 29% were accurately segmented (error between 1 and 3 mm), which demonstrates the robustness of the presented method.

CONCLUSIONS

A fully automatic method for whole heart and cardiac chamber segmentation was presented and evaluated using multicenter/multivendor CTA data. The accuracy and robustness of the method were demonstrated by successfully applying the method to 1420 multicenter/ multivendor data sets.

Semi-automatic segmentation for prostate interventions

In this paper we report and characterize a semi-automatic prostate segmentation method for prostate brachytherapy. Based on anatomical evidence and requirements of the treatment procedure, a warped and tapered ellipsoid was found suitable as the a-priori 3D shape of the prostate. By transforming the acquired endorectal transverse images of the prostate into ellipses, the shape fitting problem was cast into a convex problem which can be solved efficiently. The average whole gland error between non-overlapping volumes created from manual and semi-automatic contours from 21 patients was 6.63 ± 0.9%. For use in brachytherapy treatment planning, the resulting contours were modified, if deemed necessary, by radiation oncologists prior to treatment. The average whole gland volume error between the volumes computed from semi-automatic contours and those computed from modified contours, from 40 patients, was 5.82 ± 4.15%. The amount of bias in the physicians' delineations when given an initial semi-automatic contour was measured by comparing the volume error between 10 prostate volumes computed from manual contours with those of modified contours. This error was found to be 7.25 ± 0.39% for the whole gland. Automatic contouring reduced subjectivity, as evidenced by a decrease in segmentation inter- and intra-observer variability from 4.65% and 5.95% for manual segmentation to 3.04% and 3.48% for semi-automatic segmentation, respectively. We characterized the performance of the method relative to the reference obtained from manual segmentation by using a novel approach that divides the prostate region into nine sectors. We analyzed each sector independently as the requirements for segmentation accuracy depend on which region of the prostate is considered. The measured segmentation time is 14 ± 1s with an additional 32 ± 14s for initialization. By assuming 1-3 min for modification of the contours, if necessary, a total segmentation time of less than 4 min is required, with no additional time required prior to treatment planning. This compares favorably to the 5-15 min manual segmentation time required for experienced individuals. The method is currently used at the British Columbia Cancer Agency (BCCA) Vancouver Cancer Centre as part of the standard treatment routine in low dose rate prostate brachytherapy and is found to be a fast, consistent and accurate tool for the delineation of the prostate gland in ultrasound images.

Sharing and reusing cardiovascular anatomical models over the Web: a step towards the implementation of the virtual physiological human project

Sharing and reusing anatomical models over the Web offers a significant opportunity to progress the investigation of cardiovascular diseases. However, the current sharing methodology suffers from the limitations of static model delivery (i.e. embedding static links to the models within Web pages) and of a disaggregated view of the model metadata produced by publications and cardiac simulations in isolation. In the context of euHeart--a research project targeting the description and representation of cardiovascular models for disease diagnosis and treatment purposes--we aim to overcome the above limitations with the introduction of euHeartDB, a Web-enabled database for anatomical models of the heart. The database implements a dynamic sharing methodology by managing data access and by tracing all applications. In addition to this, euHeartDB establishes a knowledge link with the physiome model repository by linking geometries to CellML models embedded in the simulation of cardiac behaviour. Furthermore, euHeartDB uses the exFormat--a preliminary version of the interoperable FieldML data format--to effectively promote reuse of anatomical models, and currently incorporates Continuum Mechanics, Image Analysis, Signal Processing and System Identification Graphical User Interface (CMGUI), a rendering engine, to provide three-dimensional graphical views of the models populating the database. Currently, euHeartDB stores 11 cardiac geometries developed within the euHeart project consortium.

Automated border detection in three-dimensional echocardiography: principles and promises

Several automated border detection approaches for three-dimensional echocardiography have been developed in recent years, allowing quantification of a range of clinically important parameters. In this review, the background and principles of these approaches and the different classes of methods are described from a practical perspective, as well as the research trends to achieve a robust method.

Automatic segmentation of rotational x-ray images for anatomic intra-procedural surface generation in atrial fibrillation ablation procedures

Since the introduction of 3-D rotational X-ray imaging, protocols for 3-D rotational coronary artery imaging have become widely available in routine clinical practice. Intra-procedural cardiac imaging in a computed tomography (CT)-like fashion has been particularly compelling due to the reduction of clinical overhead and ability to characterize anatomy at the time of intervention. We previously introduced a clinically feasible approach for imaging the left atrium and pulmonary veins (LAPVs) with short contrast bolus injections and scan times of approximately 4 -10 s. The resulting data have sufficient image quality for intra-procedural use during electro-anatomic mapping (EAM) and interventional guidance in atrial fibrillation (AF) ablation procedures. In this paper, we present a novel technique to intra-procedural surface generation which integrates fully-automated segmentation of the LAPVs for guidance in AF ablation interventions. Contrast-enhanced rotational X-ray angiography (3-D RA) acquisitions in combination with filtered-back-projection-based reconstruction allows for volumetric interrogation of LAPV anatomy in near-real-time. An automatic model-based segmentation algorithm allows for fast and accurate LAPV mesh generation despite the challenges posed by image quality; relative to pre-procedural cardiac CT/MR, 3-D RA images suffer from more artifacts and reduced signal-to-noise. We validate our integrated method by comparing 1) automatic and manual segmentations of intra-procedural 3-D RA data, 2) automatic segmentations of intra-procedural 3-D RA and pre-procedural CT/MR data, and 3) intra-procedural EAM point cloud data with automatic segmentations of 3-D RA and CT/MR data. Our validation results for automatically segmented intra-procedural 3-D RA data show average segmentation errors of 1) approximately 1.3 mm compared with manual 3-D RA segmentations 2) approximately 2.3 mm compared with automatic segmentation of pre-procedural CT/MR data and 3) approximately 2.1 mm compared with registered intra-procedural EAM point clouds. The overall experiments indicate that LAPV surfaces can be automatically segmented intra-procedurally from 3-D RA data with comparable quality relative to meshes derived from pre-procedural CT/MR.

4-D cardiac MR image analysis: left and right ventricular morphology and function

In this study, a combination of active shape model (ASM) and active appearance model (AAM) was used to segment the left and right ventricles of normal and Tetralogy of Fallot (TOF) hearts on 4-D (3-D+time) MR images. For each ventricle, a 4-D model was first used to achieve robust preliminary segmentation on all cardiac phases simultaneously and a 3-D model was then applied to each phase to improve local accuracy while maintaining the overall robustness of the 4-D segmentation. On 25 normal and 25 TOF hearts, in comparison to the expert traced independent standard, our comprehensive performance assessment showed subvoxel segmentation accuracy, high overlap ratios, good ventricular volume correlations, and small percent volume differences. Following 4-D segmentation, novel quantitative shape and motion features were extracted using shape information, volume-time and dV/dt curves, analyzed and used for disease status classification. Automated discrimination between normal/TOF subjects achieved 90%-100% sensitivity and specificity. The features obtained from TOF hearts show higher variability compared to normal subjects, suggesting their potential use as disease progression indicators. The abnormal shape and motion variations of the TOF hearts were accurately captured by both the segmentation and feature characterization.