Sebastian Peter Michael Dries

Spine detection and labeling using a parts-based graphical model

The detection and extraction of complex anatomical structures usually involves a trade-off between the complexity of local feature extraction and classification, and the complexity and performance of the subsequent structural inference from the viewpoint of combinatorial optimization. Concerning the latter, computationally efficient methods are of particular interest that return the globally-optimal structure. We present an efficient method for part-based localization of anatomical structures which embeds contextual shape knowledge in a probabilistic graphical model. It allows for robust detection even when some of the part detections are missing. The application scenario for our statistical evaluation is spine detection and labeling in magnetic resonance images.

Automated model-based rib cage segmentation and labeling in CT images

We present a new model-based approach for an automated labeling and segmentation of the rib cage in chest CT scans. A mean rib cage model including a complete vertebral column is created out of 29 data sets. We developed a ray search based procedure for rib cage detection and initial model pose. After positioning the model, it was adapted to 18 unseen CT data. In 16 out of 18 data sets, detection, labeling, and segmentation succeeded with a mean segmentation error of less than 1.3 mm between true and detected object surface. In one case the rib cage detection failed, in another case the automated labeling.

Assessment of anterior cruciate ligament reconstruction using 3D ultrashort echo-time MR imaging

This work demonstrates the potential of ultrashort TE (UTE) imaging for visualizing graft material and fixation elements after surgical repair of soft tissue trauma such as ligament or meniscal injury. Three asymptomatic patients with anterior cruciate ligament (ACL) reconstruction using different graft fixation methods were imaged at 1.5T using a 3D UTE sequence. Conventional multislice turbo spin‐echo (TSE) measurements were performed for comparison. 3D UTE imaging yields high signal from tendon graft material at isotropic spatial resolution, thus facilitating direct positive contrast graft visualization. Furthermore, metal and biopolymer graft fixation elements are clearly depicted due to the high contrast between the signal‐void implants and the graft material. Thus, the ability of UTE MRI to visualize short‐T2 tissues such as tendons, ligaments, or tendon grafts can provide additional information about the status of the graft and its fixation in the situation after cruciate ligament repair. UTE MRI can therefore potentially support diagnosis when problems occur or persist after surgical procedures involving short‐T2 tissues and implants. J. Magn. Reson. Imaging 2009;29:443–448.

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.

Lung lobe modeling and segmentation with individualized surface meshes

An automated segmentation of lung lobes in thoracic CT images is of interest for various diagnostic purposes like the quantification of emphysema or the localization of tumors within the lung. Although the separating lung fissures are visible in modern multi-slice CT-scanners, their contrast in the CT-image often does not separate the lobes completely. This makes it impossible to build a reliable segmentation algorithm without additional information. Our approach uses general anatomical knowledge represented in a geometrical mesh model to construct a robust lobe segmentation, which even gives reasonable estimates of lobe volumes if fissures are not visible at all. The paper describes the generation of the lung model mesh including lobes by an average volume model, its adaptation to individual patient data using a special fissure feature image, and a performance evaluation over a test data set showing an average segmentation accuracy of 1 to 3 mm.

Automatic Segmentation of Cardiac CTs - Personalized Atrial Models Augmented with Electrophysiological Structures

Electrophysiological simulations of the atria could improve diagnosis and treatment of cardiac arrhythmia, like atrial fibrillation or flutter. For this purpose, a precise segmentation of both atria is needed. However, the atrial epicardium and the electrophysiological structures needed for electrophysiological simulations are barely or not at all detectable in CT-images. Therefore, a model based segmentation of only the atrial endocardium was developed as a landmark generator to facilitate the registration of a finite wall thickness model of the right and left atrial myocardium. It further incorporates atlas information about tissue structures relevant for simulation purposes like Bachmanns bundle, terminal crest, sinus node and the pectinate muscles. The correct model based segmentation of the atrial endocardium was achieved with a mean vertex to surface error of 0.53 mm for the left and 0.18 mm for the right atrium respectively. The atlas based myocardium segmentation yields physiologically correct results well suited for electrophysiological simulations.

Validation and comparison of registration methods for free-breathing 4D lung CT

We have compared and validated image registration methods with respect to the clinically relevant use-case of lung CT max-inhale to max-exhale registration. Four fundamentally different algorithms representing main approaches for image registration were compared using clinical images. Each algorithm was assigned to a different person with extensive working knowledge of its usage. Quantitative and qualitative evaluation is performed. Whereas the methods achieve similar results in target registration error, characteristic differences come to show by closer analysis of the displacement fields.

Towards automatic patient positioning and scan planning using continuously moving table MR imaging

A concept is proposed to simplify patient positioning and scan planning to improve ease of use and workflow in MR. After patient preparation in front of the scanner the operator selects the anatomy of interest by a single push‐button action. Subsequently, the patient table is moved automatically into the scanner, while real‐time 3D isotropic low‐resolution continuously moving table scout scanning is performed using patient‐independent MR system settings. With a real‐time organ identification process running in parallel and steering the scanner, the target anatomy can be positioned fully automatically in the scanners sensitive volume. The desired diagnostic examination of the anatomy of interest can be planned and continued immediately using the geometric information derived from the acquired 3D data. The concept was implemented and successfully tested in vivo in 12 healthy volunteers, focusing on the liver as the target anatomy. The positioning accuracy achieved was on the order of several millimeters, which turned out to be sufficient for initial planning purposes. Furthermore, the impact of nonoptimal system settings on the positioning performance, the signal‐to‐noise ratio (SNR), and contrast‐to‐noise ratio (CNR) was investigated. The present work proved the basic concept of the proposed approach as an element of future scan automation. Magn Reson Med, 2009.

Simultaneous Model-Based Segmentation of Multiple Objects

Deformable models are used for the segmentation of objects in 3D images by adapting flexible meshes to image structures. The simultaneous segmentation of multiple objects often causes problems like violation of spatial relationships. Here we present two methods for including prior knowledge into the segmentation process: The first deals with objects sliding with respect to each other, and the second considers a pre-defined minimal distance between neighboring objects. Using this prior knowledge improved segmentation results can be reached.

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

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