Hans-Peter Meinzer

The Medical Imaging Interaction Toolkit (MITK) - a toolkit facilitating the creation of interactive software by extending VTK and ITK

The aim of the Medical Imaging Interaction Toolkit (MITK) is to facilitate the creation of clinically usable image-based software. Clinically usable software for image-guided procedures and image analysis require a high degree of interaction to verify and, if necessary, correct results from (semi-)automatic algorithms. MITK is a class library basing on and extending the Insight Toolkit (ITK) and the Visualization Toolkit (VTK). ITK provides leading-edge registration and segmentation algorithms and forms the algorithmic basis. VTK has powerful visualization capabilities, but only low-level support for interaction (like picking methods, rotation, movement and scaling of objects). MITK adds support for high level interactions with data like, for example, the interactive construction and modification of data objects. This includes concepts for interactions with multiple states as well as undo-capabilities. Furthermore, VTK is designed to create one kind of view on the data (either one 2D visualization or a 3D visualization). MITK facilitates the realization of multiple, different views on the same data (like multiple, multiplanar reconstructions and a 3D rendering). Hierarchically structured combinations of any number and type of data objects (image, surface, vessels, etc.) are possible. MITK can handle 3D+t data, which are required for several important medical applications, whereas VTK alone supports only 2D and 3D data. The benefit of MITK is that it supplements those features to ITK and VTK that are required for convenient to use, interactive and by that clinically usable image-based software, and that are outside the scope of both. MITK will be made open-source (http://www.mitk.org).

The simplicity of metazoan cell lineages

Developmental processes are thought to be highly complex, but there have been few attempts to measure and compare such complexity across different groups of organisms. Here we introduce a measure of biological complexity based on the similarity between developmental and computer programs. We define the algorithmic complexity of a cell lineage as the length of the shortest description of the lineage based on its constituent sublineages. We then use this measure to estimate the complexity of the embryonic lineages of four metazoan species from two different phyla. We find that these cell lineages are significantly simpler than would be expected by chance. Furthermore, evolutionary simulations show that the complexity of the embryonic lineages surveyed is near that of the simplest lineages evolvable, assuming strong developmental constraints on the spatial positions of cells and stabilizing selection on cell number. We propose that selection for decreased complexity has played a major role in moulding metazoan cell lineages.

ALES: cell lineage analysis and mapping of developmental events

MOTIVATION Animals build their bodies by altering the fates of cells. The way in which they do so is reflected in the topology of cell lineages and the fates of terminal cells. Cell lineages should, therefore, contain information about the molecular events that determined them. Here we introduce new tools for visualizing, manipulating, and extracting the information contained in cell lineages. Our tools enable us to analyze very large cell lineages, where previously analyses have only been carried out on cell lineages no larger than a few dozen cells. RESULTS Ales (A Lineage Evaluation System) allows the display, evaluation and comparison of cell lineages with the aim of identifying molecular and cellular events underlying development. Ales introduces a series of algorithms that locate putative developmental events. The distribution of these predicted events can then be compared to gene expression patterns or other cellular characteristics. In addition, artificial lineages can be generated, or existing lineages modified, according to a range of models, in order to test hypotheses about lineage evolution. AVAILABILITY The program can run on any operating system with a compliant Java 2 environment. Ales is free for academic use and can be downloaded from http://mbi.dkfz-heidelberg.de/mbi/research/cellsim/ales.

Lymph node segmentation on CT images by a shape model guided deformable surface method

With many tumor entities, quantitative assessment of lymph node growth over time is important to make therapy choices or to evaluate new therapies. The clinical standard is to document diameters on transversal slices, which is not the best measure for a volume. We present a new algorithm to segment (metastatic) lymph nodes and evaluate the algorithm with 29 lymph nodes in clinical CT images. The algorithm is based on a deformable surface search, which uses statistical shape models to restrict free deformation. To model lymph nodes, we construct an ellipsoid shape model, which strives for a surface with strong gradients and user-defined gray values. The algorithm is integrated into an application, which also allows interactive correction of the segmentation results. The evaluation shows that the algorithm gives good results in the majority of cases and is comparable to time-consuming manual segmentation. The median volume error was 10.1% of the reference volume before and 6.1% after manual correction. Integrated into an application, it is possible to perform lymph node volumetry for a whole patient within the 10 to 15 minutes time limit imposed by clinical routine.

Interactive Surface Correction for 3D Shape Based Segmentation

Statistical shape models have become a fast and robust method for segmentation of anatomical structures in medical image volumes. In clinical practice, however, pathological cases and image artifacts can lead to local deviations of the detected contour from the true object boundary. These deviations have to be corrected manually. We present an intuitively applicable solution for surface interaction based on Gaussian deformation kernels. The method is evaluated by two radiological experts on segmentations of the liver in contrast-enhanced CT images and of the left heart ventricle (LV) in MRI data. For both applications, five datasets are segmented automatically using deformable shape models, and the resulting surfaces are corrected manually. The interactive correction step improves the average surface distance against ground truth from 2.43mm to 2.17mm for the liver, and from 2.71mm to 1.34mm for the LV. We expect this method to raise the acceptance of automatic segmentation methods in clinical application.

Navigation aids and real-time deformation modeling for open liver surgery

This contribution presents a novel method for image-guided navigation in oncological liver surgery. It enables the perpetuation of the registration for deeply located intrahepatic structures during the resection. For this purpose, navigation aids localizable by an electro-magnetic tracking system are anchored within the liver. Position and orientation data gained from the navigation aids are used to parameterize a real-time deformation model. This approach enables for the first time the real-time monitoring of target structures also in the depth of the intraoperatively deformed liver. The dynamic behavior of the deformation model has been evaluated with a silicon phantom. First experiments have been carried out with pig livers ex vivo.

Soft tissue navigation using needle-shaped markers: Evaluation of navigation aid tracking accuracy and CT registration

We evaluate two core modules of a novel soft tissue navigation system. The system estimates the position of a hidden target (e.g. a tumor) during a minimally invasive intervention from the location of a set of optically tracked needle-shaped navigation aids which are placed in the vicinity of the target. The initial position of the target relative to the navigation aids is obtained from a CT scan. The accuracy of the entire system depends on (a) the accuracy for locating a set of navigation aids in a CT image, (b) the accuracy for determining the positions of the navigation aids during the intervention by means of optical tracking, (c) the accuracy for tracking the applicator (e.g. the biopsy needle), and (d) the accuracy of the real-time deformation model which continuously computes the location of the initially determined target point from the current positions of the navigation aids. In this paper, we focus on the first two aspects. We introduce the navigation aids we constructed for our system and show that the needle tips can be tracked with submillimeter accuracy. Furthermore, we present and evaluate three methods for registering a set of navigation aid models with a given CT image. The fully-automatic algorithm outperforms both the manual method and the semi-automatic algorithm, yielding an average distance of 0.27 ± 0.08 mm between the estimated needle tip position and the reference position.

Soft tissue navigation for laparoscopic prostatectomy: evaluation of camera pose estimation for enhanced visualization

We introduce a novel navigation system to support minimally invasive prostate surgery. The system utilizes transrectal ultrasonography (TRUS) and needle-shaped navigation aids to visualize hidden structures via Augmented Reality. During the intervention, the navigation aids are segmented once from a 3D TRUS dataset and subsequently tracked by the endoscope camera. Camera Pose Estimation methods directly determine position and orientation of the camera in relation to the navigation aids. Accordingly, our system does not require any external tracking device for registration of endoscope camera and ultrasonography probe. In addition to a preoperative planning step in which the navigation targets are defined, the procedure consists of two main steps which are carried out during the intervention: First, the preoperatively prepared planning data is registered with an intraoperatively acquired 3D TRUS dataset and the segmented navigation aids. Second, the navigation aids are continuously tracked by the endoscope camera. The cameras pose can thereby be derived and relevant medical structures can be superimposed on the video image. This paper focuses on the latter step. We have implemented several promising real-time algorithms and incorporated them into the Open Source Toolkit MITK (www.mitk.org). Furthermore, we have evaluated them for minimally invasive surgery (MIS) navigation scenarios. For this purpose, a virtual evaluation environment has been developed, which allows for the simulation of navigation targets and navigation aids, including their measurement errors. Besides evaluating the accuracy of the computed pose, we have analyzed the impact of an inaccurate pose and the resulting displacement of navigation targets in Augmented Reality.

Evaluation and extension of a navigation system for bronchoscopy inside human lungs

For exact orientation inside the tracheobronchial tree, clinicians are in urgent need of a navigation system for bronchoscopy. Such an image guided system has the ability to show the current position of a bronchoscope (instrument to inspect the inside of the lung) within the tracheobronchial tree. Thus orientation inside the complex tree structure is improved. Our approach of navigated bronchoscopy considers the problem of using a static image to navigate inside a constantly moving soft tissue. It offers a direct guidance to a preinterventionally defined target inside the bronchial tree to save intervention time spent on searching the right path and to minimize the duration of anesthesia. It is designed to adapt to the breathing cycle of the patient, so no further intervention to minimize the movement of the lung has to stress the patient. We present a newly developed navigation sensor with allows to display a virtual bronchoscopy in real time and we demonstrate an evaluation on the accuracy within a non moving ex vivo lung phantom.

An interactive system for volume segmentation in computer-assisted surgery

Computer-assisted surgery aims at a decreased surgical risk and a reduced recovery time of patients. However, its use is still limited to complex cases because of the high effort. It is often caused by the extensive medical image analysis. Especially, image segmentation requires a lot of manual work. Surgeons and radiologists are suffering from usability problems of many workstations. In this work, we present a dedicated workplace for interactive segmentation integratd within the CHILI (tele-)radiology system. The software comes with a lot of improvements with respect to its graphical user interface, the segmentation process and the segmentatin methods. We point out important software requirements and give insight into the concepts which were implemented. Further examples and applications illustrate the software system.