Dedicated visualization methods are among the most important tools of modern computer-aided medical applications. Reformation methods such as Multiplanar Reformation or Curved Planar Reformation have evolved as useful tools that facilitate diagnostic and therapeutic work. In this paper, we present a novel approach that can be seen as a generalization of Multiplanar Reformation to curved surfaces. The main concept is to generate reformatted medical volumes driven by the individual anatomical geometry of a specific patient. This process generates flat views of anatomical structures that facilitate many tasks such as diagnosis, navigation and annotation. Our reformation framework is based on a non-linear as-rigid-as-possible volumetric deformation scheme that uses generic triangular surface meshes as input. To manage inevitable distortions during reformation, we introduce importance maps which allow controlling the error distribution and improving the overall visual quality in areas of elevated interest. Our method seamlessly integrates with well-established concepts such as the slice-based inspection of medical datasets and we believe it can improve the overall efficiency of many medical workflows. To demonstrate this, we additionally present an integrated visualization system and discuss several use cases that substantiate its benefits.

Abstract 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 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.

Abstract Accurate visualizations of complex vascular structures are essential for medical applications, such as diagnosis, therapy planning and medical education. Vascular trees are usually described using centerlines, since they capture both the topology and the geometry of the vasculature in an intuitive manner. State‐of‐the‐art vessel segmentation algorithms deliver vascular outlines as free‐form contours along the centerline, since this allows capturing anatomical pathologies. However, existing methods for generating surface representations from centerlines can only cope with circular outlines. We present a novel model‐based technique that is capable of generating intersection‐free surfaces from centerlines with complex outlines. Vascular segments are described by local signed distance functions and combined using Boolean operations. An octree‐based surface generation strategy automatically computes watertight, scale‐adaptive meshes with a controllable quality. In contrast to other approaches, our method generates a reliable representation that guarantees to capture all vessels regardless of their size.

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.

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. Advance 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, adn 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.

We present a fast hardware-accelerated stippling method which does not require any preprocessing for placing points on surfaces. The surfaces are automatically parameterized in order to apply stippling textures without major distortions. The mapping process is guided by a decomposition of the space in cubes. Seamless scaling with a constant density of points is realized by subdividing and summarizing cubes. Our mip-map technique enables arbitrarily scaling with one texture. Different shading tones and scales are facilitated by adhering to the constraints of tonal art maps. With our stippling technique, it is feasible to encode all scaling and brightness levels within one self-similar texture. Our method is applied to surfaces extracted from (segmented) medical volume data. The speed of the stippling process enables stippling for several complex objects simultaneously. We consider application scenarios in intervention planning (neck and liver surgery planning). In these scenarios, object recognition (shape perception) is supported by adding stippling to semi-transparently shaded objects which are displayed as context information.

Viewpoint selection is crucial for medical intervention planning. The interactive exploration of a scene with 3d objects involves the systematic analysis of several anatomic structures. Viewpoint selection techniques enhance the display of the currently selected structure. For animations in collaborative intervention planning and surgical education, the authoring process may be significantly enhanced if ‘good' viewpoints for important objects as well as for the whole scene are chosen automatically.We describe a viewpoint selection technique guided by parameters like size of unoccluded surface, importance of occluding objects, preferred region and viewpoint stability. The influence of these parameters may be flexibly adjusted by weights. Parameter maps indicate the influence of the current parameter settings on the viewpoints. For selected applications, the weights may be predefined and reused for other cases. We also describe an informal user study which was accomplished to understand if our viewpoint selection strategies produce adequate results from the users' point of view.

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.

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.

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.

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.