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Objective: The identification of tumor infiltrated brain tissue during neurosurgical resection is still a major challenge in the field of neurooncology. The challenge is aggravated by the infiltrative growth of certain tumor types, like glioblastoma multiforme, which leads to ill-defined tumor borders. [for full text, please go to the a.m. URL]

Objective: Optical coherence tomography (OCT) is a non-invasive technique that can capture 3-dimensional images with micrometer resolution. Prior studies of several groups have shown that OCT has the potential to detect glial tumors by evaluating changes in tissue structure and/or optical properties[for full text, please go to the a.m. URL], 71. Jahrestagung der Deutschen Gesellschaft für Neurochirurgie (DGNC), 9. Joint Meeting mit der Japanischen Gesellschaft für Neurochirurgie

Background: Optical coherence tomography (OCT) is indispensable for studying the progression of age-related macular degeneration (AMD), since it can show quantitatively morphological changes of the retina. However, current OCT devices can only be used in clinical settings and do not provide functional[for full text, please go to the a.m. URL], 7th International Symposium on AMD: Age-related Macular Degeneration - Understanding Pathogenetic Mechanisms of Disease

In radiotherapy of abdominal and thoracic tumors, respiratory motion is a problem for an accurate treatment. Most current motion compensation techniques rely on externally acquired breathing signals of the patient. The systems in clinical use usually work with 1D surface motion signals to describe internal structure respiratory motion patterns. As a 1D signal is not able to describe complex motion patterns and breathing variations, in this work the Microsoft Kinect, which can record multidimensional respiratory surface motion signals, is proposed to be used instead. For the Kinect, a clinically acceptable measurement setup is designed and Kinect measurements are compared to the Varian RPM system (clinical standard). The results show that the signals are well aligned. An additional comparison of Kinect signals from different regions of interest on the chest further reveals variations between them. This illustrates that the use of a system that provides multidimensional signals is worthwhile; the knowledge about breathing variations could be applied for optimization of current clinical workflows.

Einleitung: In der Radiologie wie in vielen anderen medizinischen Disziplinen werden Befundberichte überwiegend durch Freitext-Diktate generiert. Dadurch wird die Auswertung und rechnergestützte Weiterverarbeitung von Befundinhalten erschwert. Außerdem ergeben sich aus der unausweichlichen[for full text, please go to the a.m. URL], GMDS 2013; 58. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS)

Einleitung: Die Methoden interaktiver graphischer Benutzeroberflächen (GUIs) in Anwendungssoftwaresystemen werden bis heute implizit und zusammen mit der Anwendungslogik im Sourcecode repräsentiert. Die Wiederverwendung, Weiterentwicklung und Modifikation der Oberflächen gestaltet sich[for full text, please go to the a.m. URL], GMDS 2013; 58. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS)

Introduction: Radiotherapy of thoracic and abdominal tumors has to cope with breathing-induced motion as a major source of localisation uncertainties. State-of-the-art problem-solving approaches like gating or tracking usually rely on external breathing signals [ref:1]. Assuming a (linear)[for full text, please go to the a.m. URL], GMDS 2013; 58. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS)

Introduction: Magnetic resonance imaging is a non-invasive and radiation-free modality, which offers spatial and temporal image data acquisition. To deal with the resulting increased number of data (semi-)automatic image processing methods are required. With our approach, we present a method for combined[for full text, please go to the a.m. URL], GMDS 2013; 58. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS)

Introduction: Segmentation of lungs and lung lobes is a prerequisite for pulmonary image analysis/diagnostics (e.g. detection of abnormalities in the lungs) [ref:1]. It therefore attracts a great interest (cf. the LOLA11 workshop, [link:http://www.lola11.com/#http://www.lola11.com/]),[for full text, please go to the a.m. URL], GMDS 2012; 57. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS)

For diagnosing a stenosis or an aneurysm, the shape parameters of the diseased vessel parts are needed by physicians. Therefore, a fully-automatic extraction of this shape from a volume representation has been developed. This paper analyses the blood vessel branch labeling acceleration algorithms and proposes two improved methods. The first one is called the surface wave propagation method which restricts the wave moving along the blood vessel surface. The second one replaces the thinning algorithm with the surface propagation to extract the center lines and the bifurcations of the blood vessels. Experiment results show that two improved methods can decrease the computation time and keep the labeling accuracy.

The subcellular localisation of proteins in living cells is a crucial means for the determination of their function. We propose an approach to realise such a protein localisation based on microscope images. In order to reach this goal, appropriate features are selected. Then, the initial feature set is optimised by a genetic algorithm. The actual classification of possible protein localisations is accomplished by an incremental neural network which not only achieves a very high accuracy, but enables on-line learning, as well.

In diesem Beitrag werden drei Verfahren zur automatischen Segmentierung von ungefärbten, lebenden Zellen in Hellfeld-Bildern verglichen. Auf der Grundlage dieser Segmente soll in Zukunft eine Lokalisation subzellulärer Strukturen in parallel dazu aufgenommenen Fluoreszenzbildern stattfinden. Neben der Wasserscheidentransformation wurden zwei eigene Ansätze auf der Basis aktiver Konturen untersucht. Als Vergleichskriterien dienten der Segmentierungsfehler im Bezug zu Referenzsegmenten, die von biologischen Experten erstellt wurden, sowie die benötigte Rechenzeit. Das Ziel ist hierbei eine an unsere Problemstellung angepasste Auswahl des Segmentierungsverfahrens. Die gewonnenen Erkenntnisse sind jedoch auch für alternative Anwendungen vorteilhaft, da wesentliche Eigenschaften der Segmentierungsverfahren deutlich werden.

In diesem Beitrag wird ein Verfahren zur automatischen Lokalisation und Segmentierung ungefärbter Zellen in Hellfeld-Mikroskopbildern vorgestellt. Hierfür erfolgt insbesondere eine Nutzung von aktiven Konturen und morphologischen Operatoren, da diese Techniken eine Einbeziehung von Vorwissen über den Zellaufbau und die Zellform zulassen und somit Ansätzen, die ausschließlich auf der Auswertung von Bildpunkt-Intensitäten basieren, überlegen sind. Die verwendeten Algorithmen wurden dabei an die spezifischen Eigenschaften von Hellfeld-Bildern, wie z.B. starke Störungen, angepasst.

In the field of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of breast cancer, current research efforts in computer-aided diagnosis (CADx) are mainly focused on the temporal series of T 1-weighted images acquired during uptake of a contrast agent, processing morphological and kinetic information. Although static T 2-weighted images are usually part of DCE-MRI protocols, they are seldom used in CADx systems. The aim of this work was to evaluate to what extent T 2-weighted images provide complementary information to a CADx system, improving its performance for the task of discriminating benign breast masses from life-threatening carcinomas. In a preliminary study considering 64 masses, inclusion of lesion features derived from T 2-weighted images increased the classification performance from A z =0.94 to A z =0.99.

Current virtual reality (VR) training simulators of liver punctures often rely on static 3D patient data and use an unrealistic (sinusoidal) periodic animation of the respiratory movement. Existing methods for the animation of breathing motion support simple mathematical or patient-specific, estimated breathing models. However with personalized breathing models for each new patient, a heavily dose relevant or expensive 4D data acquisition is mandatory for keyframe-based motion modeling. Given the reference 4D data, first a model building stage using linear regression motion field modeling takes place. Then the methodology shown here allows the transfer of existing reference respiratory motion models of a 4D reference patient to a new static 3D patient. This goal is achieved by using non-linear inter-patient registration to warp one personalized 4D motion field model to new 3D patient data. This cost- and dose-saving new method is shown here visually in a qualitative proof-of-concept study., Comment: World Society for Computer Graphics - WSCG 2017 publication, 9 pages, 5 figures, 1 movie online

The identification and segmentation of focal hyperintense lesions on magnetic resonance images (MRI) are essential steps in the assessment of disease burden in multiple sclerosis (MS) patients. Manual lesion segmentation is considered to be the gold standard, although it is time-consuming and has poor intra- and inter-operator reproducibility. Here, we present a segmentation method based on dual-echo MR images initialized by manual identification of lesions and a priori information. The classification technique is based on a region growing approach with a final segmentation refinement step. The results have revealed high similarity between the segmentation performed with this method and that performed manually by an expert operator, as well as a low misclassification of lesions. Moreover, the time required for segmentation is drastically reduced.