Cristian Lorenz

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.

Evaluation of Registration Methods on Thoracic CT: The EMPIRE10 Challenge

EMPIRE10 (Evaluation of Methods for Pulmonary Image REgistration 2010) is a public platform for fair and meaningful comparison of registration algorithms which are applied to a database of intra patient thoracic CT image pairs. Evaluation of nonrigid registration techniques is a nontrivial task. This is compounded by the fact that researchers typically test only on their own data, which varies widely. For this reason, reliable assessment and comparison of different registration algorithms has been virtually impossible in the past. In this work we present the results of the launch phase of EMPIRE10, which comprised the comprehensive evaluation and comparison of 20 individual algorithms from leading academic and industrial research groups. All algorithms are applied to the same set of 30 thoracic CT pairs. Algorithm settings and parameters are chosen by researchers expert in the con figuration of their own method and the evaluation is independent, using the same criteria for all participants. All results are published on the EMPIRE10 website (http://empire10.isi.uu.nl). The challenge remains ongoing and open to new participants. Full results from 24 algorithms have been published at the time of writing. This paper details the organization of the challenge, the data and evaluation methods and the outcome of the initial launch with 20 algorithms. The gain in knowledge and future work are discussed.

Multi-scale line segmentation with automatic estimation of width, contrast and tangential direction in 2D and 3D medical images

A new multi-scale segmentation technique for line-like structures in 2D and 3D medical images is presented. It is based on normalized first and second derivatives and on the eigenvector analysis of the hessian matrix. Application areas are the segmentation and tracking of bloodvessels, electrodes, catheters and other line-like objects. It allows for the estimation of the local diameter, the longitudinal direction and the contrast of the vessel and for the distinction between edge-like and line-like structures. The method is applicable as automatic 2D and 3D line-filter, as well as for interactive algorithms that are based on local direction estimation. A 3D line-tracker has been constructed that uses the estimated longitudinal direction as step-direction. After extraction of the centerline, the hull of the structure is determined by a 2D active-contour algorithm, applied in planes, orthogonal to the longitudinal line-direction. The procedure results in a stack of contours allowing quantitative crosssection area determination and visualization by means of a triangulation based rendering.

Automated model-based vertebra detection, identification, and segmentation in CT images

For many orthopaedic, neurological, and oncological applications, an exact segmentation of the vertebral column including an identification of each vertebra is essential. However, although bony structures show high contrast in CT images, the segmentation and labelling of individual vertebrae is challenging. In this paper, we present a comprehensive solution for automatically detecting, identifying, and segmenting vertebrae in CT images. A framework has been designed that takes an arbitrary CT image, e.g., head-neck, thorax, lumbar, or whole spine, as input and provides a segmentation in form of labelled triangulated vertebra surface models. In order to obtain a robust processing chain, profound prior knowledge is applied through the use of various kinds of models covering shape, gradient, and appearance information. The framework has been tested on 64 CT images even including pathologies. In 56 cases, it was successfully applied resulting in a final mean point-to-surface segmentation error of 1.12+/-1.04mm. One key issue is a reliable identification of vertebrae. For a single vertebra, we achieve an identification success of more than 70%. Increasing the number of available vertebrae leads to an increase in the identification rate reaching 100% if 16 or more vertebrae are shown in the image.

Generation of Point-Based 3D Statistical Shape Models for Anatomical Objects

A novel method that allows the development of surface point-based three-dimensional statistical shape models is presented. Given a set of medical objects, a statistical shape model can be obtained by principal component analysis. This technique requires that a set of complex shaped objects is represented as a set of vectors that uniquely determines the shapes of the objects and at the same time is suitable for a statistical analysis. The correspondence between the vector components and the respective shape features has to be identical in order for all shape parameter vectors to be considered. We present a novel approach to the correspondence problem for arbitrary three-dimensional objects which involves developing a template shape and fitting this template to all objects to be analyzed. The method is successfully applied to obtain a statistical shape model for the lumbar vertebrae.

An approach to 2D/3D registration of a vertebra in 2D X-ray fluoroscopies with 3D CT images

In order to use pre-operative images during an operation for navigation, they must be registered to the patients coordinate system in the operating theater or to an intra-operative image. One problem in this area is the registration of a vertebra in intra-operatively acquired x-ray fluoroscopies with 3D CT images obtained before the intervention. The result can be used to support the placement of pedicle screws in spine surgery or stents in Transfemoral Endovascular Aneurysm Management (TEAM). For this 2D/3D registration task a novel voxel-based method is presented. Using a small part of the CT image covering the vertebra only, pseudo projections are computed and the resulting vertebra template is compared to the x-ray projection. A new similarity measure was introduced for that purpose, because commonly used measures did not work. The method allows for a much faster implementation than other voxel-based 2D/3D registration approaches, because they use the entire CT image to calculate pseudo projections. Unlike contour-based 2D/3D registration approaches, the method does not require segmentation of the vertebras contours in the x-ray projection. Application and performance of the proposed registration method are demonstrated by application to images of a TEAM procedure.

Impact of four-dimensional computed tomography pulmonary ventilation imaging-based functional avoidance for lung cancer radiotherapy.

PURPOSE To quantify the dosimetric impact of four-dimensional computed tomography (4D-CT) pulmonary ventilation imaging-based functional treatment planning that avoids high-functional lung regions. METHODS AND MATERIALS 4D-CT ventilation images were created from 15 non-small-cell lung cancer patients using deformable image registration and quantitative analysis of the resultant displacement vector field. For each patient, anatomic and functional plans were created for intensity-modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT). Consistent beam angles and dose-volume constraints were used for all cases. The plans with Radiation Therapy Oncology Group (RTOG) 0617-defined major deviations were modified until clinically acceptable. Functional planning spared the high-functional lung, and anatomic planning treated the lungs as uniformly functional. We quantified the impact of functional planning compared with anatomic planning using the two- or one-tailed t test. RESULTS Functional planning led to significant reductions in the high-functional lung dose, without significantly increasing other critical organ doses, but at the expense of significantly degraded the planning target volume (PTV) conformity and homogeneity. The average reduction in the high-functional lung mean dose was 1.8 Gy for IMRT (p < .001) and 2.0 Gy for VMAT (p < .001). Significantly larger changes occurred in the metrics for patients with a larger amount of high-functional lung adjacent to the PTV. CONCLUSION The results of the present study have demonstrated the impact of 4D-CT ventilation imaging-based functional planning for IMRT and VMAT for the first time. Our findings indicate the potential of functional planning in lung functional avoidance for both IMRT and VMAT, particularly for patients who have high-functional lung adjacent to the PTV.

Simultaneous segmentation and tree reconstruction of the airways for virtual bronchoscopy

During the last couple of years virtual endoscopic systems (VES) have emerged as standard tools that are nowadays close to be utilized in daily clinical practice. Such tools render hollow human structures, allowing a clinician to visualize their inside in an endoscopic-like paradigm. It is common practice that the camera of a virtual endoscope is attached to the centerline of the structure of interest, to facilitate navigation. This centerline has to be determined manually or automatically, prior to an investigation. While there exist techniques that can straightforwardly handle simple tube-like structures (e.g. colon, aorta), structures like the tracheobronchial tree still represent a challenge due to their complex branching. In these cases it is necessary to determine all branching points within the tree which is - because of the complexity - impractical to be accomplished in a manual manner. This paper presents a simultaneous segmentation/skeletonization algorithm that extracts all major airway branches and large parts of the minor distal branches (up to 7th order) using a front propagation approach. During the segmentation the algorithm keeps track of the centerline of the segmented structure and detects all branching points. This in turn allows the full reconstruction of the tracheobronchial tree.

A Multi-scale Line Filter with Automatic Scale Selection Based on the Hessian Matrix for Medical Image Segmentation

A multi-scale segmentation technique for line-like structures in 2D and 3D medical images is presented. It is based on normalized second derivatives and on the eigenvector analysis of the Hessian matrix. The method allows for the estimation of the local diameter, the longitudinal direction and the contrast of line-structures and for the distinction between edge-like and line-like structures. The characteristics of the method in respect to several analytic line-profiles as well as the influence of neighboring structures and line-bending is discussed. The method is applied to 3D medical images.

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.