Olivier Ecabert

Automatic Model-Based Segmentation of the Heart in CT Images

Automatic image processing methods are a pre-requisite to efficiently analyze the large amount of image data produced by computed tomography (CT) scanners during cardiac exams. This paper introduces a model-based approach for the fully automatic segmentation of the whole heart (four chambers, myocardium, and great vessels) from 3-D CT images. Model adaptation is done by progressively increasing the degrees-of-freedom of the allowed deformations. This improves convergence as well as segmentation accuracy. The heart is first localized in the image using a 3-D implementation of the generalized Hough transform. Pose misalignment is corrected by matching the model to the image making use of a global similarity transformation. The complex initialization of the multicompartment mesh is then addressed by assigning an affine transformation to each anatomical region of the model. Finally, a deformable adaptation is performed to accurately match the boundaries of the patients anatomy. A mean surface-to-surface error of 0.82 mm was measured in a leave-one-out quantitative validation carried out on 28 images. Moreover, the piecewise affine transformation introduced for mesh initialization and adaptation shows better interphase and interpatient shape variability characterization than commonly used principal component analysis.

Optimizing boundary detection via Simulated Search with applications to multi-modal heart segmentation

Segmentation of medical images can be achieved with the help of model-based algorithms. Reliable boundary detection is a crucial component to obtain robust and accurate segmentation results and to enable full automation. This is especially important if the anatomy being segmented is too variable to initialize a mean shape model such that all surface regions are close to the desired contours. Several boundary detection algorithms are widely used in the literature. Most use some trained image appearance model to characterize and detect the desired boundaries. Although parameters of the boundary detection can vary over the model surface and are trained on images, their performance (i.e., accuracy and reliability of boundary detection) can only be assessed as an integral part of the entire segmentation algorithm. In particular, assessment of boundary detection cannot be done locally and independently on model parameterization and internal energies controlling geometric model properties. In this paper, we propose a new method for the local assessment of boundary detection called Simulated Search. This method takes any boundary detection function and evaluates its performance for a single model landmark in terms of an estimated geometric boundary detection error. In consequence, boundary detection can be optimized per landmark during model training. We demonstrate the success of the method for cardiac image segmentation. In particular we show that the Simulated Search improves the capture range and the accuracy of the boundary detection compared to a traditional training scheme. We also illustrate how the Simulated Search can be used to identify suitable classes of features when addressing a new segmentation task. Finally, we show that the Simulated Search enables multi-modal heart segmentation using a single algorithmic framework. On computed tomography and magnetic resonance images, average segmentation errors (surface-to-surface distances) for the four chambers and the trunks of the large vessels are in the order of 0.8 mm. For 3D rotational X-ray angiography images of the left atrium and pulmonary veins, the average error is 1.3 mm. In all modalities, the locally optimized boundary detection enables fully automatic segmentation.

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.

Automatic whole heart segmentation in static magnetic resonance image volumes

We present a fully automatic segmentation algorithm for the whole heart (four chambers, left ventricular myocardium and trunks of the aorta, the pulmonary artery and the pulmonary veins) in cardiac MR image volumes with nearly isotropic voxel resolution, based on shape-constrained deformable models. After automatic model initialization and reorientation to the cardiac axes, we apply a multi-stage adaptation scheme with progressively increasing degrees of freedom. Particular attention is paid to the calibration of the MR image intensities. Detailed evaluation results for the various anatomical heart regions are presented on a database of 42 patients. On calibrated images, we obtain an average segmentation error of 0.76mm.

Adaptive Hough transform for the detection of natural shapes under weak affine transformations

This paper introduces a two-steps adaptive generalized Hough transform (GHT) for the detection of non-analytic objects undergoing weak affine transformations in images. The first step of our algorithm coarsely locates the region of interest with a GHT for similitudes. The returned detection is then used by an adaptive GHT for affine transformations. The adaptive strategy makes the computation more amenable and ensures high accuracy, while keeping the size of the accumulator array small. To account for the deformable nature of natural objects, local shape variability is incorporated into the algorithm in both the detection and reconstruction steps. Finally, experiments are performed on real medical data showing that both accuracy and reasonable computation times can be reached.

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 ~ 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) ~ 1.3 mm compared with manual 3-D RA segmentations (2) ~ 2.3 mm compared with automatic segmentation of pre-procedural CT/MR data and (3) ~ 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.

Modeling shape variability for full heart segmentation in cardiac computed-tomography images

An efficient way to improve the robustness of the segmentation of medical images with deformable models is to use a priori shape knowledge during the adaptation process. In this work, we investigate how the modeling of the shape variability in shape-constrained deformable models influences both the robustness and the accuracy of the segmentation of cardiac multi-slice CT images. Experiments are performed for a complex heart model, which comprises 7 anatomical parts, namely the four chambers, the myocardium, and trunks of the aorta and the pulmonary artery. In particular, we compare a common shape variability modeling technique based on principal component analysis (PCA) with a more simple approach, which consists of assigning an individual affine transformation to each anatomical subregion of the heart model. We conclude that the piecewise affine modeling leads to the smallest segmentation error, while simultaneously offering the largest flexibility without the need for training data covering the range of possible shape variability, as required by PCA.

Toward fully automatic object detection and segmentation

An automatic procedure for detecting and segmenting anatomical objects in 3-D images is necessary for achieving a high level of automation in many medical applications. Since todays segmentation techniques typically rely on user input for initialization, they do not allow for a fully automatic workflow. In this work, the generalized Hough transform is used for detecting anatomical objects with well defined shape in 3-D medical images. This well-known technique has frequently been used for object detection in 2-D images and is known to be robust and reliable. However, its computational and memory requirements are generally huge, especially in case of considering 3-D images and various free transformation parameters. Our approach limits the complexity of the generalized Hough transform to a reasonable amount by (1) using object prior knowledge during the preprocessing in order to suppress unlikely regions in the image, (2) restricting the flexibility of the applied transformation to only scaling and translation, and (3) using a simple shape model which does not cover any inter-individual shape variability. Despite these limitations, the approach is demonstrated to allow for a coarse 3-D delineation of the femur, vertebra and heart in a number of experiments. Additionally it is shown that the quality of the object localization is in nearly all cases sufficient to initialize a successful segmentation using shape constrained deformable models.

Integrating Viability Information into a Cardiac Model for Interventional Guidance

It has been demonstrated that 3D anatomical models can be used effectively as roadmaps in image guided interventions. However, besides the anatomical information also the integrated display of functional information is desirable. In particular, a number of procedures such as the treatment of coro nary artery disease by revascularization and myocardial repair by targeted cell delivery require information about myocardial viability. In this paper we show how we can determine myocardial viability and integrate the information into a patient-specific cardiac 3D model. In contrast to other work we associate the viability information directly with the 3D patient anatomy. Thus we ensure that the functional information can be visualized in a way suitable for interventional guidance. Furthermore we propose a workflow that allows the nearly automatic generation of the patient-specific model. Our work is based on a previously published cardiac model that can be automatically adapted to images from different modalities like CT and MR. To enable integration of myocardial viability we first define a new myocardium surface model that encloses the left ventricular cavity in a way that suits robust viability measurements. We modify the model-based segmentation method to allow accurate adaptation of this new model. Second, we extend the model and the segmentation method to incorporate volumetric tissue properties. We validate the accuracy of the segmentation of the left ventricular cavity systematically using clinical data and illustrate the complete method for integrating myocardial viability by an example.

Towards automatic full heart segmentation in computed-tomography images

We present a robust, fast and fully automatic approach enabling the segmentation of the main anatomical structures of the heart in CT images. The proposed method is based on the adaptation of a 3D triangulated mesh to new unknown images exploiting simultaneously knowledge of organ shape and typical gray level appearance in images, both learned from a training database made of 28 data sets. The described approach was tested on more than 50 volume images at different cardiac phases. Visual inspection by experts reveals that the proposed method is overall robust and succeeds in segmenting the heart up to minor interactive local corrections