Automatic heart segmentation in CT: current and future applications

Transcription

1 Clinical applications Automatic heart segmentation in CT: current and future applications O. Ecabert J. Peters J.Weese Cristian Lorenz Jens von Berg M.J.Walker M.E. Olszewski M.Vembar Philips Research Europe, Aachen, Germany. Philips Research Europe, Hamburg, Germany. Philips Medical Systems, CT Clinical Science, Cleveland, USA. Cardiac CT is still the most challenging of all clinical applications. Robust automatic segmentation of the cardiac anatomy is a key enabler. 12 MEDICAMUNDI 50/3 2006/12 Computed Tomography (CT) is rapidly establishing itself as the non-invasive modality of choice for imaging the heart. Emerging clinical applications include CT guidance for electrophysiology procedures, planning for the correction of structural heart disease such as patent foramen ovale or septum defects, and percutaneous valve replacement. The introduction of spiral scanning in 1990 [1,2] provided high-resolution imaging within a single breath hold. The acquisition time was drastically reduced with the introduction of multislice scanners (4-channel in 1998, 16-channel in 2001, 40-channel in 2003 and 64-channel in 2004), thus paving the way for cardiac applications [3-9]. The cardiac region of interest can now be aquired in as little as six seconds. However, with the increase in the number of detector rows, the large amount of data generated poses a workflow and diagnostic challenge to the clinician, with potentially over 4000 axial images being generated for each cardiac study. In spite of the technical advances, cardiac CT is still the most challenging of all CT applications. The heart is continually moving, and the optimal cardiac phases have to be selected for reconstruction. Image characteristics are strongly influenced by inter- (and even intra-) patient variability in cardiac morphology, and individual patient s anatomy (heart rate, body mass index (BMI), shape, etc). Abnormalities, transplanted hearts, and hearts with pathology can present an unusual appearance. Acquisition protocols and reconstruction parameters have a direct repercussion on image quality. Good quality images require careful patient preparation and accurate timing of the contrast injection. Thus, there is a significant need for workflow support in routine clinical practice. Robust automatic segmentation of various structures of the cardiac anatomy is a key enabler for improving the clinical workflow. Segmentation refers to the identification of the various anatomical structures present in the image, with delineation of the boundaries or labeling of the voxels enclosed by the boundaries. Once segmentation has been performed, it is possible to extract clinical parameters such as ventricular mass, ejection fraction and wall thickness. Identification of the various cardiac structures also makes it to retrieve and display any desired view virtually instantaneously. Consequently, automatic segmentation can significantly reduce the scan-to-diagnosis time, thus helping the clinicians to reach the fundamental goal of efficient patient management. This article presents a fully automatic cardiac segmentation algorithm, and describes its use in current and emerging cardiac applications. The segmentation approach comprises two phases: a learning phase and the actual segmentation process. In the first phase, a wealth of anatomical and technical information is acquired from a representative set of data, yielding a basic cardiac model (Figure 1). During the segmentation process, the heart is first automatically detected and then the boundaries of the heart in the image are matched to the model, resulting in accurate labeling of the anatomical structures. Heart model Figure 1 shows the heart model used for segmentation. The geometry is described by a multi-compartmental mesh consisting of 15,000 triangles [10,11]. The model comprises the left and right ventricles, the left and right atria, the left-ventricular myocardium, and the trunks of the pulmonary artery and the aorta. In addition, information about important

2 1 Figure 1. Heart model used for segmentation.the model comprises seven anatomical regions (four cardiac chambers, myocardium, trunks of the pulmonary artery and of the aorta) indicated by distinct colours. Additionally, anatomical information (like landmarks) is also encoded into the model. anatomical landmarks such as the valve planes is encoded into the model. Inter-patient and inter-phase shape variability can be efficiently modeled by assigning an affine transformation to each part of the model [12]. Affine transformations cover translation, rotation, scaling along different coordinate axes and shearing. Mesh regularity is maintained by interpolation of the affine transformations at the transitions between the anatomical regions. The accuracy of this piecewise affine model was evaluated using reference meshes that were semi-automatically adapted by experts to 28 images originating from 13 patients at various cardiac phases. The piecewise affine adaptation of the model to these meshes showed that the heart anatomy of different individuals and different heart phases can be approximated with a mean residual point-to-surface error of 1.3 mm. In addition, technical information is encoded into the heart model that enables the robust matching to images. Most important is the information for detecting the proper image boundary that is associated with each triangle [13]. The starting point to generate this information is the training set of 28 heart segmentations, along with the corresponding CT images mentioned above. In addition, a huge number of more than 500 boundary detection functions have been generated that look for image gradients and check various gray value ranges. For each dataset, each triangle and each boundary detection function, we simulate the boundary detection process that occurs during adaptation. The distances between the detected boundary positions and the desired object boundary from the reference segmentations are recorded for each tested function and each triangle. Finally, each triangle is assigned the boundary detection function corresponding to the smallest average distance. Segmentation chain The automatic segmentation of the heart consists of four steps. First, the heart is detected using the generalized Hough transformation [14], which is an algorithm for detecting an object defined by a number of representative surface points in an image. A specific aspect in this step is that the surface points result from hearts of different individuals with different shapes. The algorithm is very reliable and was able to correctly detect the heart in 149 of 150 test images. The detection only failed in one patient who had a severe aortic root aneurysm. Secondly, the heart model is matched to the image, optimizing its position in space (pose) with respect to a rigid transformation (i.e. translation, rotation and isotropic scaling).thirdly, the different anatomical regions of the model are simultaneously matched to the image using the piecewise affine transformation introduced above. Finally, accurate boundary delineation is achieved using a deformable adaptation (see Intermezzo: Mesh Adaptation). The resulting segmentation can deviate from the heart model and its variability defined by the affine Automatic segmentation of the heart consists of four steps. MEDICAMUNDI 50/3 2006/12 13

3 INTERMEZZO: Mesh adaptation In model-based segmentation a triangulated mesh is deformed to the actual patient anatomy represented in the image. For the special purpose of heart segmentation, the deformable structure consists of a multi-compartmental mesh comprising 15,000 triangles. The model comprises the left and right ventricles, the left and right atria, the left-ventricular myocardium, the trunks of the pulmonary artery and the aorta. The mesh is matched to the heart by minimizing the distance between the triangles and points detected at the object boundaries (see Figure I). The matching process is performed in two steps. First, object boundaries are searched for along profiles perpendicular to the mesh triangles. Typically, the boundary detection functions attached to each triangle are evaluated at discrete positions and the position with the most promising response is selected as the edge candidate. The next step is global minimization of the sum of the squared distances between triangle centres and edge candidates. To prevent the triangles from becoming stuck at the target positions, displacements lateral to the object boundary are permitted (i.e., the triangles are free to slide along the green line in Figure I). In steps 2 and 3 of the segmentation chain (see main article, Figure 2) vertices are constrained to deform with respect to a parametric transformation. That is, the goal of the minimization of the external energy is to find the optimum parameters of a global parametric transformation. However, in step 4, the vertices are allowed to deviate from a parametric shape description. In that case, we ensure that the resulting shape remains similar to a heart by penalizing deviations from the learned shape (Figure II). This contribution is referred to as internal energy. The segmentation problem consists now of finding the optimum vertex positions which minimize the balance between image matching (external energy) and shape prior (internal energy). In Figure I the organ of interest is represented by the light blue heart and the mesh by dark blue points connected by lines. (In three dimensions, a mesh consists of points connected in triangles.) Boundary points are searched for along profiles perpendicular to each triangle. The point with the most prominent response (green point) is selected as boundary candidate. In the illustration, d is the distance between the triangle centre and the plane parallel to the object at the detected boundary (green line). The mesh is then attracted to the object by minimizing the sum of the squared distances d projected onto the boundary normal vector. The resulting effect is to move the mesh towards the object boundary while allowing cost-free lateral sliding. In deformable adaptation, the vertices are allowed to Figure I. Matching a mesh to an image. I 14 MEDICAMUNDI 50/3 2006/12

4 deform freely. However, to prevent implausible deformation, the vertex displacements can be constrained by shape priors (Figure II). Mathematically, this can be implemented by minimizing the sum of squared differences between edge vectors in the deforming mesh (red) and prior shape model (green). To remove the influence of global positioning, both the deforming mesh and the shape model are previously aligned using point-based registration represented by the transformation T [ ]. The result of the segmentation is given by the minimum of the combined energy Eext + Eint where balances the contributions II Figure II. Constraining deformation using prior shape knowledge. transformations, but the magnitude of the deviations is limited in order to preserve the basic shape. Figure 2 shows the increasing segmentation accuracy for the different steps. Typically, the time for a complete segmentation is about 25 s on an up-to-date PC. Evaluation Segmentation accuracy was measured using cross-evaluation with the 28 reference images used before. To avoid bias due to having several images from one patient, the model was trained using the images from all but one patient and the error was measured on the cyclically omitted images. To quantify the error between the adapted and reference meshes, we used the mean Euclidean distance between the triangle centers and patches of geodesic radius r = 10 mm around the corresponding triangle centers in the other mesh. This distance was also computed interchanging the reference and adapted meshes, since it is not symmetric. 2 Figure 2.Axial slice showing the segmentation state after each stage of the segmentation chain. MEDICAMUNDI 50/3 2006/12 15

5 Figure 3. Selection of cardiac data sets along with the automatic segmentation results (no user interaction).the algorithm could successfully segment these images despite noise, irregular contrast agent distribution between the chambers, variation in field-of-view and craniocaudal coverage.the segmentation was not disturbed by hearts lying partially out of the field of view. 3 These distances were averaged for the distinct structures of the heart model. Some triangles within or close to the artificial endings of the truncated vessels are poorly defined (i.e. not related to anatomical structures) and were removed during error measurement. The resulting mean segmentation error for the whole mesh was 0.82 ± 1.00 mm. The largest mean error per anatomical region was measured for the endocardium of the left ventricle (0.98 ± 1.32 mm) due to ambiguities related to the definition of the papillary muscle borders. Typical segmentation results provided by our algorithm with no user interaction are shown on Figure 3. Future applications The heart segmentation can be used in numerous different ways to support the presentation of cardiac CT images, their reconstruction, inspection, and planning of interventions. Figure 4 illustrates some examples. By converting the heart segmentation into a volume annotation, it is relatively easy to derive all kinds of measurements, such as true three-dimensional chamber volumes, end-systolic and end-diastolic phases, ejection fraction or ventricular mass. Apart from its relevance as clinical parameters, this information can be used in the imaging chain, for instance, to select the optimum cardiac phase. Furthermore, the volume annotation can be used to assign different transparencies and opacities to the parts of the heart for volume visualization. The landmarks encoded in the model can be used to establish cardiac coordinate systems and thus automatically generate standard cardiac displays such as short-axis or four-chamber views. The access to various landmarks within the cardiac anatomy opens up new applications for imaging modalities in the interventional area. This will be of an immense help to electro- Figure 4. Examples illustrating the future use of the heart model.the heart model can be converted into a volume annotation (upper left) allowing for all different kinds of volume measurements.the volume annotation can also be used within volume rendering (upper middle), where the different anatomical parts of the heart appear with a different color. Model landmarks allow automatic generation of standard cardiac displays such as four-chamber views (upper right). Further examples are the overlay of the heart model onto interventional X-ray images and the automatic generation of a rendering from the inside of the left atrium to support guidance during electrophysiological procedures MEDICAMUNDI 50/3 2006/12

6 physiologists when performing procedures such as radio-frequency (RF) ablation of the pulmonary veins. An anatomical overlay of a three-dimensional structure obtained from CT onto the fluoroscopic image would provide the clinician with additional information such as the number and the ostial location of pulmonary veins, size of the lasso catheter to be used, etc. Alternatively, an endoscopic view of the chamber (i.e. left atrium) with a fly-through capability would provide the clinician with additional three-dimensional information such as the curvature of the internal structures. Here, the heart segmentation can help to automatically find the center of the atrium and define a proper viewing direction. Outlook We have demonstrated a robust model-based cardiac segmentation algorithm that can address an important need in routine clinical practice. This is expected to be a key enabler in creating and addressing new opportunities in the entire image chain, in which the current approach to handling a large amount of data poses a significant challenge. In addition, this can also be extended to other modalities such as ultrasound and magnetic resonance imaging, and adapted to other anatomical structures, thus facilitating the use of a single robust image processing approach that can act as an enabler for new clinical applications in the future. Acknowledgement The authors would like to thank H. Schramm (Philips Research Europe Aachen, Germany) for his essential contributions to heart detection and generalized Hough transform. Further thanks go to G. Lavi and J. Lessick (Philips Medical Systems, Advanced Technology Center, Haifa, Israel) for continuous support regarding evaluation and integration A robust model-based segmentation algorithm can be an enabler for new applications. References [1] Kalender WA, Seissler W, Klotz E, Vock P. Spiral Volumetric CT with Single Breath-Hold Technique, Continuous Transport and Continuous Scanner Rotation. Radiology 1990; 176: [2] Crawford CR, King KF. Computed Tomography Scanning with Simultaneous Patient Translation. Med Phys 1990; 17: [3] Ohnesorge B, Flohr T, Becker C, Kopp AF, Schoeph UJ, Baum U et al. Cardiac Imaging by means of Electrocardiographically Gated Multi- Section Spiral CT: Initial Experience. Radiology 2001; 217: [4] Hoffmann MHK, Shi H, Manzke R, Schmid FT, De Vries L, Grass M et al. Non-Invasive Coronary Angiography with 16-Detector Row CT: Effect of Heart Rate. Radiology 2005; 234: [5] Hoffmann MHK, Shi H, Schmitz BL, Schmid FT, Lieberknecht M, Shulze R et al. Noninvasive Coronary Angiography with Multislice Computed Tomography. JAMA 2005; 293: [6] Garcia MJ, Lessick J, Hoffmann MHK, for the CATSCAN Study Investigators. Accuracy of 16-Row Multidetector Computed Tomography for the Assessment of Coronary Artery Stenosis. JAMA 2006; 296: [7] Gaspar T, Peled N. Cardiac and Vascular Imaging using 40-Slice CT: Initial Experiences. Medicamundi 2004; 48,1: [8] Watkins MW, Hesse B, Green CE, Greenberg NL, Manning M, Chaudhry E et al. Detection of Coronary Artery Stenosis using 40- Channel Computed Tomography using Multi-Segment Reconstruction. Accepted by AJC, January [9] Rubinshtein R, Gaspar T, Halon DA, Goldstein J, Peled N, Lewis BS. Prevalence and Extent of Obstructive Coronary Disease in Patients with Zero or Low Calcium Score undergoing 64-Slice Cardiac Multidetector Computed Tomography for Evaluation of a Chest Pain Syndrome. Accepted by AJC, [10] Von Berg J and Lorenz C. Multi-Surface Cardiac Modeling, Segmentation, and Tracking. Proc. Functional Imaging and Modeling of the Heart. Ser. LNCS 2005 (Frangi AF, Radeva P, Santos A, Hernandez M Eds) 3504: [11]Lorenz C, Von Berg J. A Comprehensive Shape Model of the Heart. Medical Image Analysis 2006; 10: [12] Ecabert O, Peters J, Weese J. Modeling Shape Variability for Full Heart Segmentation in Cardiac CT Images. Proceedings SPIE Medical Imaging 2006 (Reinhardt JM, Pluim JPW Eds) 6144: R R 12. [13] Peters J, Ecabert O, Weese J. Feature Optimization via Simulated Search for Model-Based Heart Segmentation. Proceedings CARS, Ser. ICS 2005 (Lemke HU, Inamura K, Doi K, Vannier MW, Farman AG Eds) 1281: [14] Ballard DH. Generalizing the Hough Transform to Detect Arbitrary Shapes. Pattern Recognition 1981; 13,2: [15] Weese J, Kaus MR, Lorenz C, Lobregt S, Truyen R, Pekar V. Shape Constrained Deformable Models for 3D Medical Image Segmentation. Image Processing in Medical Imaging (IPMI), Ser. LNCS 2001 (Insana MY, Leahy RM Eds) 2082: