Milo Hindennach

Multiple hypothesis template tracking of small 3D vessel structures

A multiple hypothesis tracking approach to the segmentation of small 3D vessel structures is presented. By simultaneously tracking multiple hypothetical vessel trajectories, low contrast passages can be traversed, leading to an improved tracking performance in areas of low contrast. This work also contributes a novel mathematical vessel template model, with which an accurate vessel centerline extraction is obtained. The tracking is fast enough for interactive segmentation and can be combined with other segmentation techniques to form robust hybrid methods. This is demonstrated by segmenting both the liver arteries in CT angiography data, which is known to pose great challenges, and the coronary arteries in 32 CT cardiac angiography data sets in the Rotterdam Coronary Artery Algorithm Evaluation Framework, for which ground-truth centerlines are available.

Lung lobe segmentation by anatomy-guided 3D watershed transform

Since the lobes are mostly independent anatomic compartments of the lungs, they play a major role in diagnosis and therapy of lung diseases. The exact localization of the lobe-separating fissures in CT images often represents a non-trivial task even for experts. Therefore, a lung lobe segmentation method suitable to work robustly under clinical conditions must take advantage of additional anatomic information. Due to the absence of larger blood vessels in the vicinity of the fissures, a distance transform performed on a previously generated vessel mask allows a reliable estimation of the boundaries even in cases where the fissures themselves are invisible. To make use of image regions with visible fissures, we linearly combine the original data with the distance map. The segmentation itself is performed on the combined image using an interactive 3D watershed algorithm which allows an iterative refinement of the results. The proposed method was successfully applied to CT scans of 24 patients. Preliminary intra- and inter-observer studies conducted for one of the datasets showed a volumetric variability of well below 1%. The achieved structural decomposition of the lungs not only assists in subsequent image processing steps but also allows a more accurate prediction of lobe-specific functional parameters.

Template-based multiple hypotheses tracking of small vessels

A template tracking approach to the segmentation of small 3D vessel structures is presented. The main contributions are a general formulation of a vessel template function and a multiple hypotheses tracking framework that is shown to improve the tracking robustness. The methodology is demonstrated using CT angiography data of the liver to which a hybrid region growing and tracking segmentation is applied.

Interactive determination of robust safety margins for oncologic liver surgery

ObjectiveComplex oncologic interventions in the liver require an extensive and careful preoperative analysis. Particularly the achievement of an optimal safety margin around tumors remains a difficult task for surgeons.MethodsWe present new methods for evaluating different safety margins and their effect on the associated interruption of vascular supply or drainage. The characteristic of vascular risk distributions can be evaluated in real-time by exploiting precomputed safety maps that provide a volume curve for each vascular system. By applying fast visualization methods in 3D it is possible to assist the surgeon in the determination of a tumor-free safety margin while preserving sufficient vital hepatic parenchyma. The combination of risk analysis from different vascular systems and their sensitivity is considered.ResultsWe provide physicians with a novel computer-aided planning tool that allows for interactive determination of safety margins in real-time. The planning tool integrates smoothly into the preoperative workflow. Preliminary evaluations confirm that the width of safety margins can be determined more precisely, which may affect the proposed resection strategy.ConclusionOur new methods provide interactive feedback and support for decision making during the preoperative planning stage and thus might potentially improve the outcome of surgical interventions.

Clinical relevance of model based computer-assisted diagnosis and therapy

The ability to acquire and store radiological images digitally has made this data available to mathematical and scientific methods. With the step from subjective interpretation to reproducible measurements and knowledge, it is also possible to develop and apply models that give additional information which is not directly visible in the data. In this context, it is important to know the characteristics and limitations of each model. Four characteristics assure the clinical relevance of models for computer-assisted diagnosis and therapy: ability of patient individual adaptation, treatment of errors and uncertainty, dynamic behavior, and in-depth evaluation. We demonstrate the development and clinical application of a model in the context of liver surgery. Here, a model for intrahepatic vascular structures is combined with individual, but in the degree of vascular details limited anatomical information from radiological images. As a result, the model allows for a dedicated risk analysis and preoperative planning of oncologic resections as well as for living donor liver transplantations. The clinical relevance of the method was approved in several evaluation studies of our medical partners and more than 2900 complex surgical cases have been analyzed since 2002.

Intraoperative adaptation and visualization of preoperative risk analyses for oncologic liver surgery

Tumor resections from the liver are complex surgical interventions. With recent planning software, risk analyses based on individual liver anatomy can be carried out preoperatively. However, additional tumors within the liver are frequently detected during oncological interventions using intraoperative ultrasound. These tumors are not visible in preoperative data and their existence may require changes to the resection strategy. We propose a novel method that allows an intraoperative risk analysis adaptation by merging newly detected tumors with a preoperative risk analysis. To determine the exact positions and sizes of these tumors we make use of a navigated ultrasound-system. A fast communication protocol enables our application to exchange crucial data with this navigation system during an intervention. A further motivation for our work is to improve the visual presentation of a moving ultrasound plane within a complex 3D planning model including vascular systems, tumors, and organ surfaces. In case the ultrasound plane is located inside the liver, occlusion of the ultrasound plane by the planning model is an inevitable problem for the applied visualization technique. Our system allows the surgeon to focus on the ultrasound image while perceiving context-relevant planning information. To improve orientation ability and distance perception, we include additional depth cues by applying new illustrative visualization algorithms. Preliminary evaluations confirm that in case of intraoperatively detected tumors a risk analysis adaptation is beneficial for precise liver surgery. Our new GPU-based visualization approach provides the surgeon with a simultaneous visualization of planning models and navigated 2D ultrasound data while minimizing occlusion problems.

METK — The Medical Exploration Toolkit

In the following we will describe concept and realization of the Medical Exploration Toolkit — the METK. The METK is designed for loading, visualizing and exploring segmented medical data sets. It is a framework of several modules based on the free MeVisLab, a development environment for medical image processing and visualization. The framework is platform-independent and freely available. We will also present several different applications, developed with the METK.

Integration automatischer Abstandsberechnungen in die Interventionsplanung

Wir stellen ein Verfahren aus der Robotik vor, mit dem minimale Abstande zwischen polygonalen 3D-Objekten effizient bestimmt werden konnen. Dabei beschreiben wir eine empirisch bestimmte Parametrisierung, die sich als besonders gunstig erwiesen hat. Fur das bei der Interventionsplanung wichtige Problem der Bestimmung eines minimalen Abstandes zwischen Gefasen und Tumoren wird eine spezielle Losung beschrieben, die eine Filterung der Gefasvoxel basierend auf einem Kriterium fur den Gefasdurchmesser beinhaltet.

3D-Lungenlappen-Segmentierung durch Kombination von Region Growing, Distanz- und Wasserscheiden-Transformation

Die Lungenlappen spielen als annahernd unabhangige anatomische Komponenten der Lunge eine wesentliche Rolle bei Diagnose und Therapie von Lungenerkrankungen. Eine Detektion der dunnen Lappengrenzen, der sogenannten Fissuren ist jedoch schwierig, da diese in vielen Fallen aufgrund pathologischer Veranderungen nur unvollstandig im CT-Bild erscheinen. Daher bestimmt unser Ansatz die Lappengrenzen im Wesentlichen auf Basis der lappenspezifischen Gefassysteme und verwendet die eventuell vorhandene Reprasentation der Fissuren in den Daten lediglich als Zusatzinformation. Die Methode benotigt dabei minimale und intuitive Interaktion und erlaubt eine robuste Dekomposition der Lunge in ihre Lappen, welche vor allem zur Bestimmung lappenspezifischer CT-Parameter verwendet werden kann.