Biomedical subjects

Jens von Berg

Publications and source records attributed to Jens von Berg.

3 recordsLinked to original sources

PubMed2006

A comprehensive shape model of the heart.

Domain knowledge about the geometrical properties of cardiac structures is an important ingredient for the segmentation of these structures in medical images or for the simulation of cardiac physiology. So far, a strong focus was put on the left ventricle due to its importance for the general pumping performance of the heart and related functional indices. However, other cardiac structures are of similar importance, e.g., the coronary arteries with respect to diagnosis and treatment of arteriosclerosis or the left atrium with respect to the treatment of atrial fibrillation. In this paper we describe the generation of a geometric cardiac model including the four cardiac chambers and the trunks of the connected vasculature, as well as the coronary arteries and a set of cardiac landmarks. A mean geometric model for the end-diastolic heart has been built based on 27 cardiac CT datasets and has been evaluated with respect to its capability to estimate the position of cardiac structures. Allowing a similarity transformation to adapt the model to image data, cardiac surface positions can be predicted with an accuracy of below 5mm.

Computer Simulation↗

PubMed2004

Automated segmentation of the left ventricle in cardiac MRI.

We present a fully automated deformable model technique for myocardium segmentation in 3D MRI. Loss of signal due to blood flow, partial volume effects and significant variation of surface grey value appearance make this a difficult problem. We integrate various sources of prior knowledge learned from annotated image data into a deformable model. Inter-individual shape variation is represented by a statistical point distribution model, and the spatial relationship of the epi- and endocardium is modeled by adapting two coupled triangular surface meshes. To robustly accommodate variation of grey value appearance around the myocardiac surface, a prior parametric spatially varying feature model is established by classification of grey value surface profiles. Quantitative validation of 121 3D MRI datasets in end-diastolic (end-systolic) phase demonstrates accuracy and robustness, with 2.45 mm (2.84 mm) mean deviation from manual segmentation.

Algorithms↗

PubMed2002

Texture segmentation performance related to cortical geometry.

There are two prevailing explanations for the foveal deficit in texture segmentation reported in previous works. One is based on the spatial and temporal properties of the stimuli, which means in terms of physiology a strong contribution of the Magno-channel. The other one is purely spatial and assigns filters of different bandwidths to each eccentricity in the visual field. We have challenged the first explanation experimentally by using isoluminant stimuli. The central performance drop persisted although the Magno-channel is known to respond weakly to stimuli with low luminance contrast. Therefore, we agreed with the spatial explanation. But instead of the abstract filter theories from previous works we propose a computational neural model assuming local lateral interactions in a cortical map model. The psychophysical performance measures could be directly related to geometric properties of the primary visual cortex concerning its mapping geometry and its intrinsic interaction width. Our model accounts quantitatively for our own psychophysical data as well as for others from literature. In general, we claim that the high foveal retino-cortical magnification maps texture elements too far away from each other for being compared by local processes.

Contrast Sensitivity↗