Christian Tietjen

Combining silhouettes, surface, and volume rendering for surgery education and planning

We introduce a flexible combination of volume, surface, and line rendering. We employ object-based edge detection because this allows a flexible parametrization of the generated lines. Our techniques were developed mainly for medical applications using segmented patient-individual volume datasets. In addition, we present an evaluation of the generated visualizations with 8 medical professionals and 25 laypersons. Integration of lines in conventional rendering turned out to be appropriate.

Interactive visualization for neck-dissection planning

In this paper, we present visualization techniques for neck dissection planning. These interventions are carried out to remove lymph node metastasis in the neck region. 3d visualization is intended to explore and to quantify anatomic and pathologic structures and thus support decisions concerning the surgical strategy. For this purpose we developed and combined visualization and interaction techniques such as cutaway views, silhouettes and colorcoded distances. In addition, a standardized procedure for processing and visualization of the patient data is presented.

Integration of measurement tools in medical 3d visualizations

We discuss 3d interaction techniques for the quantitative analysis of spatial relations in medical visualizations. We describe the design and implementation of measurement tools to measure distances, angles and volumes in 3d visualizations. The visualization of measurement tools as recognizable 3d objects and a 3d interaction, which is both intuitive and precise, determines the usability of such facilities. Measurements may be carried out in 2d visualizations of the original radiological data and in 3d visualizations. The result of a measurement carried out in one view is also displayed in the other view appropriately. We discuss the validation of the obtained measures. Finally, we describe how some important measurement tasks may be solved automatically.

Automatic Detection and Segmentation of Focal Liver Lesions in Contrast Enhanced CT Images

In this paper a novel system for automatic detection and segmentation of focal liver lesions in CT images is presented. It utilizes a probabilistic boosting tree to classify points in the liver as either lesion or parenchyma, thus providing both detection and segmentation of the lesions at the same time and fully automatically. To make the segmentation more robust, an iterative classification scheme is integrated, that incorporates knowledge gained from earlier iterations into later decisions. Finally, a comprehensive evaluation of both the segmentation and the detection performance for the most common hypo dense lesions is given. Detection rates of 77% could be achieved with a sensitivity of 0.95 and a specificity of 0.93 for lesion segmentation at the same settings.

Precise segmentation of multiple organs in CT volumes using learning-based approach and information theory

In this paper, we present a novel method by incorporating information theory into the learning-based approach for automatic and accurate pelvic organ segmentation (including the prostate, bladder and rectum). We target 3D CT volumes that are generated using different scanning protocols (e.g., contrast and non-contrast, with and without implant in the prostate, various resolution and position), and the volumes come from largely diverse sources (e.g., diseased in different organs). Three key ingredients are combined to solve this challenging segmentation problem. First, marginal space learning (MSL) is applied to efficiently and effectively localize the multiple organs in the largely diverse CT volumes. Second, learning techniques, steerable features, are applied for robust boundary detection. This enables handling of highly heterogeneous texture pattern. Third, a novel information theoretic scheme is incorporated into the boundary inference process. The incorporation of the Jensen-Shannon divergence further drives the mesh to the best fit of the image, thus improves the segmentation performance. The proposed approach is tested on a challenging dataset containing 188 volumes from diverse sources. Our approach not only produces excellent segmentation accuracy, but also runs about eighty times faster than previous state-of-the-art solutions. The proposed method can be applied to CT images to provide visual guidance to physicians during the computer-aided diagnosis, treatment planning and image-guided radiotherapy to treat cancers in pelvic region.

A learning based deformable template matching method for automatic rib centerline extraction and labeling in CT images

The automatic extraction and labeling of the rib centerlines is a useful yet challenging task in many clinical applications. In this paper, we propose a new approach integrating rib seed point detection and template matching to detect and identify each rib in chest CT scans. The bottom-up learning based detection exploits local image cues and top-down deformable template matching imposes global shape constraints. To adapt to the shape deformation of different rib cages whereas maintain high computational efficiency, we employ a Markov Random Field (MRF) based articulated rigid transformation method followed by Active Contour Model (ACM) deformation. Compared with traditional methods that each rib is individually detected, traced and labeled, the new approach is not only much more robust due to prior shape constraints of the whole rib cage, but removes tedious post-processing such as rib pairing and ordering steps because each rib is automatically labeled during the template matching. For experimental validation, we create an annotated database of 112 challenging volumes with ribs of various sizes, shapes, and pathologies such as metastases and fractures. The proposed approach shows orders of magnitude higher detection and labeling accuracy than state-of-the-art solutions and runs about 40 seconds for a complete rib cage on the average.

Enhancing slice-based visualizations of medical volume data

Slice-based visualizations of CT and MRI data are frequently used for diagnosis, intervention planning and intraoperative navigation since they allow a precise analysis and localization. We present new techniques to enhance the visualization of cross sectional medical image data. Our work is focussed on intervention planning and intraoperative navigation. We address the following problems of slice-based visualization in these areas: the lack of a graphical overview on the positions of anatomic structures, the localization of a target structure and the display of safety zones around pathologic structures. To improve the overview, we introduce LIFTCHARTs, attached as vertical bars to a slice-based visualization. For localizing target structures, we introduce halos. These techniques restrict the occlusion of the original data to a minimum and avoid any modification of the original data. To demonstrate the usability of these visualization techniques, we show two application scenarios in which the techniques come into operation.

Sketch-Based Editing Tools for Tumour Segmentation in 3D Medical Images

In the past years sophisticated automatic segmentation algorithms for various medical image segmentation problems have been developed. However, there are always cases where automatic algorithms fail to provide an acceptable segmentation. In these cases the user needs efficient segmentation editing tools, a problem which has not received much attention in research. We give a comprehensive overview on segmentation editing for three‐dimensional (3D) medical images. For segmentation editing in two‐dimensional (2D) images, we discuss a sketch‐based approach where the user modifies the segmentation in the contour domain. Based on this 2D interface, we present an image‐based as well as an image‐independent method for intuitive and efficient segmentation editing in 3D in the context of tumour segmentation in computed tomography (CT). Our editing tools have been evaluated on a database containing 1226 representative liver metastases, lung nodules and lymph nodes of different shape, size and image quality. In addition, we have performed a qualitative evaluation with radiologists and technical experts, proving the efficiency of our tools.

The Medical Exploration Toolkit: An Efficient Support for Visual Computing in Surgical Planning and Training

Application development is often guided by the usage of software libraries and toolkits. For medical applications, the toolkits currently available focus on image analysis and volume rendering. Advanced interactive visualizations and user interface issues are not adequately supported. Hence, we present a toolkit for application development in the field of medical intervention planning, training, and presentation-the MEDICALEXPLORATIONTOOLKIT (METK). The METK is based on the rapid prototyping platform MeVisLab and offers a large variety of facilities for an easy and efficient application development process. We present dedicated techniques for advanced medical visualizations, exploration, standardized documentation, and interface widgets for common tasks. These include, e.g., advanced animation facilities, viewpoint selection, several illustrative rendering techniques, and new techniques for object selection in 3D surface models. No extended programming skills are needed for application building, since a graphical programming approach can be used. The toolkit is freely available and well documented to facilitate the use and extension of the toolkit.

GPU-based smart visibility techniques for tumor surgery planning

PurposeThe rating of distances and infiltrations to vital structures is important for the planning of tumor surgery or interventional procedures. To support such an assessment, the target structures should be clearly emphasized in a 3D visualization by ensuring their visibility.MethodsSmart Visibility techniques such as Ghosting Views and Breakaway Views are employed. Ghosting Views highlight focus structures by fading out occluding structures and are often used in anatomical illustrations. Breakaway Views reveal the structure by cutting into surrounding structures. As a result, an intersection surface is created that allows relating the focus structure with its surroundings. In this contribution, a specialized GPU-based implementation of these techniques is presented for polygonal models derived from a segmentation of the anatomical structures.ResultsWe present different rendering styles of the techniques and apply them to highlight enlarged lymph nodes in the neck, as well as tumors inside the liver. Compared to other methods, we focus on polygonal models and optimizations. Thus, very high frame rates could be achieved on consumer graphics hardware. Furthermore, we employed markers that support the estimation of distances within the scene and possible infiltrations around the focus structures.ConclusionThe parameters for the techniques are defined automatically to aid the employment in clinical routine. Such an application is also supported by the combination and refinement of established rendering techniques.