Although there are several data analysis frameworks, both commercial and open source, supporting the detection of tumours on nuclear magnetic resonance (NMR) sequences, none of them gives satisfactory results in the case of low volume tumors. The majority of the frameworks require the detailed analysis of at least two sequences of the examined sample, or give sample specific thresholds distinguishing between the tumor and subtypes of healthy tissue. In this paper, we present a novel algorithm for the automated estimation of tumor specific cut-off values in the domain of the apparent diffusion coefficient (ADC). Once the cut-off characteristics for a particular type of tumor is estimated, their further usage on other independent samples does not require any calculations except for an easy thresholding. The proposed methodology is a combination of classical decomposition of ADC distribution into a Gaussian mixture model (GMM) with k-means clustering subsequently performed on the parameters of mixture model components, leading to the identification of ADC distributions for every tissue type. The maximum conditional probability criterion gives the final threshold estimate. The developed signal analysis pipeline was applied to the problem of Glioblastoma Multiforme grade IV brain tumor segmentation, with a dataset of 119 randomly chosen ADC maps and Leave-One-Out cross-validation procedure for population error estimate. Additionally, a comparison to standard GMM based tumor segmentation algorithms as well as to three other automated segmentation methods was performed and the obtained tumor regions were referenced to the segmentation done by a human expert. The results demonstrate the average MiMSeg similarity to the expert-curated decision measured by the Dice coefficient as equal to 89.2% (with 95% confidence interval 87.7 ÷ 90.6). The MiMSeg algorithm significantly outperforms other techniques in the case of small tumors (of volume less than 10%), obtaining similarity to the expert-curated decision at the level 86.7%, with 44.9% obtained by standard GMM, 79.0% by Self-Organising-Maps algorithm, 68.7% by Murakami’s algorithm, and 78.2% by Kang’s method.

Since its introduction in the early 1990s, the Iterative Closest Point (ICP) algorithm has become one of the most well-known methods for geometric alignment of 3D models. Given two roughly aligned shapes represented by two point sets, the algorithm iteratively establishes point correspondences given the current alignment of the data and computes a rigid transformation accordingly. From a statistical point of view, however, it implicitly assumes that the points are observed with isotropic Gaussian noise. In this paper, we show that this assumption may lead to errors and generalize the ICP such that it can account for anisotropic and inhomogenous localization errors. We 1) provide a formal description of the algorithm, 2) extend it to registration of partially overlapping surfaces, 3) prove its convergence, 4) derive the required covariance matrices for a set of selected applications, and 5) present means for optimizing the runtime. An evaluation on publicly available surface meshes as well as on a set of meshes extracted from medical imaging data shows a dramatic increase in accuracy compared to the original ICP, especially in the case of partial surface registration. As point-based surface registration is a central component in various applications, the potential impact of the proposed method is high.

Range imaging modalities, such as time-of-flight cameras (ToF), are becoming popular for the acquisition of intra-operative data, which can be used for registering the patient's anatomy with pre-operative data (e.g. Computed Tomography or Magnetic Resonance Imaging). As pre- and intra-operative data are acquired with different modalities, and are subject to different sources of error (systematic errors and noise), matching them is a challenging problem. We present a method for the registration of multi-modal surfaces, which is robust to high noise levels and is applicable to incomplete surfaces. An evaluation with simulated and real data demonstrates that the presented method is very accurate and is fast enough for intra-operative purposes.

This paper presents a comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images. It is based on results from the "MICCAI 2007 Grand Challenge" workshop, where 16 teams evaluated their algorithms on a common database. A collection of 20 clinical images with reference segmentations was provided to train and tune algorithms in advance. Participants were also allowed to use additional proprietary training data for that purpose. All teams then had to apply their methods to 10 test datasets and submit the obtained results. Employed algorithms include statistical shape models, atlas registration, level-sets, graph-cuts and rule-based systems. All results were compared to reference segmentations five error measures that highlight different aspects of segmentation accuracy. All measures were combined according to a specific scoring system relating the obtained values to human expert variability. In general, interactive methods reached higher average scores than automatic approaches and featured a better consistency of segmentation quality. However, the best automatic methods (mainly based on statistical shape models with some additional free deformation) could compete well on the majority of test images. The study provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.

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.

Echocardiography (cardiac ultrasound) is today the predominant technique for quantitative assessment of cardiac function and valvular heart lesions. Segmentation of cardiac structures is required to determine many important diagnostic parameters. As the heart is a moving organ, reliable information can be obtained only from three-dimensional (3-D) data over time (3-D + time = 4-D). Due to their size, the resulting four-dimensional (4-D) data sets are not reasonably accessible to simple manual segmentation methods. Automatic segmentation often yields unsatisfactory results in a clinical environment, especially for ultrasonic images. We describe a semiautomated segmentation algorithm (ROPES) that is able to greatly reduce the time necessary for user interaction and its application to extract various parameters from 4-D echocardiographic data. After searching for candidate contour points, which have to fulfill a multiscale edge criterion, the candidates are connected by minimizing a cost function to line segments that then are connected to form a closed contour. The contour is automatically checked for plausibility. If necessary, two correction methods that can also be used interactively are applied (fitting of other line segments into the contour and searching for additional candidates with a relaxed criterion). The method is validated using in vivo transesophageal echocardiographic data sets.

This paper presents telemedicine as an extension of a teleradiology framework through tools for virtual surgery. To classify the described methods and applications, the research field of virtual reality (VR) is broadly reviewed. Differences with respect to technical equipment, methodological requirements and areas of application are pointed out. Desktop VR, augmented reality, and virtual reality are differentiated and discussed in some typical contexts of diagnostic support, surgical planning, therapeutic procedures, simulation and training. Visualization techniques are compared as a prerequisite for virtual reality and assigned to distinct levels of immersion. The advantage of a hybrid visualization kernel is emphasized with respect to the desktop VR applications that are subsequently shown. Moreover, software design aspects are considered by outlining functional openness in the architecture of the host system. Here, a teleradiology workstation was extended by dedicated tools for surgical planning through a plug-in mechanism. Examples of recent areas of application are introduced such as liver tumor resection planning, diagnostic support in heart surgery, and craniofacial surgery planning. In the future, surgical planning systems will become more important. They will benefit from improvements in image acquisition and communication, new image processing approaches, and techniques for data presentation. This will facilitate preoperative planning and intraoperative applications.

This paper proceeds from the definition of teleradiology. It identifies three different generations of teleradiology systems and includes those systems that are not regarded as teleradiology systems by the authors. A list of requirements pertinent to users of first-generation teleradiology systems is introduced. Most of the requirements have been realized in a new generation teleradiology system called CHILI.

No abstract available.

Discusses and debates the role played by 3D visualization in medicine as a set of methods and techniques for displaying 3D spatial information related to the anatomy and the physiology of the human body.>