For the computer-aided diagnosis of tumor diseases knowledge about the position, size and type of the lymph
nodes is needed to compute the tumor classification (TNM). For the computer-aided planning of subsequent
surgeries like the Neck Dissection spatial information about the lymph nodes is also important. Thus, an
efficient and exact segmentation method for lymph nodes in CT data is necessary, especially pathological altered
lymph nodes play an important role here.
Based on prior work, in this paper we present a noticeably enhanced model-based segmentation method for
lymph nodes in CT data, which now can be used also for enlarged and mostly well separated necrotic lymph
nodes. Furthermore, the kind of pathological variation can be determined automatically during segmentation,
which is important for the automatic TNM classification.
Our technique was tested on 21 lymph nodes from 5 CT datasets, among several enlarged and necrotic ones.
The results lie in the range of the inter-personal variance of human experts and improve the results of former
work again. Bigger problems were only noticed for pathological lymph nodes with vague boundaries due to
infiltrated neighbor tissue.
KEYWORDS: Data modeling, Binary data, Image segmentation, Tumors, Visualization, Natural surfaces, 3D modeling, Medical imaging, Tissues, Visual process modeling
Surface models from medical image data (intensity, binary) are used for evaluating spatial relationships for
intervention or radiation treatment planning. Furthermore, surface models are employed for generating volume
meshes for simulating e.g. tissue deformation or blood flow. In such applications, smoothness and accuracy
of the models are essential. These aspects may be influenced by image preprocessing, the mesh generation
algorithm and mesh postprocessing (smoothing, simplification). Thus, we evaluated the influences of different
image preprocessing methods (Gaussian smoothing, morphological operators, shape-based interpolation), model
generation (Marching Cubes, Constrained Elastic Surface Nets, MPU Implicits) and mesh postprocessing to
intensity and binary data with respect to its application within surgical planning and simulation. The resulting
surface meshes are evaluated regarding their smoothness, accuracy and mesh quality. We consider the local
curvature, equi-angle skewness, (Hausdorff) distances between two meshes (before and after processing), and
volume preservation as measures. We discuss these results concerning their suitability for different applications
in the field of surgical planning as well as finite element simulations and make recommendations on how to receive
smooth and accurate surface meshes for exemplary cases.
A major field in cognitive neuroscience investigates neuronal correlates of human decision-making processes [1, 2]. Is it
possible to predict a decision before it is actually revealed by the volunteer? In the presented manuscript we use a
standard paradigm from economic behavioral research that proved emotional influences on human decision making: the
Ultimatum Game (UG). In the UG, two players have the opportunity to split a sum of money. One player is deemed the
proposer and the other, the responder. The proposer makes an offer as to how this money should be split between the
two. The second player can either accept or reject this offer. If it is accepted, the money is split as proposed. If rejected,
then neither player receives anything.
In the presented study a real-time fMRI system was used to derive the brain activation of the responder. Using a
Relevance-Vector-Machine classifier it was possible to predict if the responder will accept or reject an offer. The
classification result was presented to the operator 1-2 seconds before the volunteer pressed a button to convey his
decision. The classification accuracy reached about 70% averaged over six subjects.
Functional MR imaging (fMRI) enables to detect different activated brain areas according to the performed
tasks. However, data are usually evaluated after the experiment, which prohibits intra-experiment optimization
or more sophisticated applications such as biofeedback experiments. Using a human-brain-interface (HBI), subjects
are able to communicate with external programs, e.g. to navigate through virtual scenes, or to experience
and modify their own brain activation. These applications require the real-time analysis and classification of
activated brain areas.
Our paper presents first results of different strategies for real-time pattern analysis and classification realized
within a flexible experiment control system that enables the volunteers to move through a 3D virtual scene in
real-time using finger tapping tasks, and alternatively only thought-based tasks.
KEYWORDS: Statistical analysis, Functional magnetic resonance imaging, Magnetic resonance imaging, Scanners, Optical spheres, Brain activation, Data acquisition, Hemodynamics, Visualization, Control systems
The real-time analysis of brain activation using functional MRI data offers a wide range of new experiments such
as investigating self-regulation or learning strategies. However, besides special data acquisition and real-time data
analysing techniques such examination requires dynamic and adaptive stimulus paradigms and self-optimising
MRI-sequences.
This paper presents an approach that enables the unified handling of parameters influencing the different software
systems involved in the acquisition and analysing process. By developing a custom-made Experiment Description
Language (EDL) this concept is used for a fast and flexible software environment which treats aspects like
extraction and analysis of activation as well as the modification of the stimulus presentation. We describe how
extracted real-time activation is subsequently evaluated by comparing activation patterns to previous acquired
templates representing activated regions of interest for different predefined conditions. According to those results
the stimulus presentation is adapted.
The results showed that the developed system in combination with EDL is able to reliably detect and evaluate
activation patterns in real-time. With a processing time for data analysis of about one second the approach is
only limited by the natural time course of the hemodynamic response function of the brain activation.
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