Dr. Carsten Meyer Profile

Dr. Carsten Meyer

at Philips Research

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Proceedings Article | 11 March 2011 Paper

Fully automatic segmentation of complex organ systems: example of trachea, esophagus and heart segmentation in CT images

Carsten Meyer, Jochen Peters, Jürgen Weese

Proceedings Volume 7962, 796216 (2011) https://doi.org/10.1117/12.877247

KEYWORDS: Image segmentation, Esophagus, Heart, Data modeling, Process modeling, Complex systems, Computed tomography, Target detection, Image processing algorithms and systems, Systems modeling

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Automatic segmentation is a prerequisite to efficiently analyze the large amount of image data produced by modern imaging modalities. Many algorithms exist to segment individual organs or organ systems. However, new clinical applications and the progress in imaging technology will require the segmentation of more and more complex organ systems composed of a number of substructures, e.g., the heart, the trachea, and the esophagus. The goal of this work is to demonstrate that such complex organ systems can be successfully segmented by integrating the individual organs into a general model-based segmentation framework, without tailoring the core adaptation engine to the individual organs. As an example, we address the fully automatic segmentation of the trachea (around its main bifurcation, including the proximal part of the two main bronchi) and the esophagus in addition to the heart with all chambers and attached major vessels. To this end, we integrate the trachea and the esophagus into a model-based cardiac segmentation framework. Specifically, in a first parametric adaptation step of the segmentation workflow, the trachea and the esophagus share global model transformations with adjacent heart structures. This allows to obtain a robust, approximate segmentation for the trachea even if it is only partly inside the field-of-view, and for the esophagus in spite of limited contrast. The segmentation is then refined in a subsequent deformable adaptation step. We obtained a mean segmentation error of about 0.6mm for the trachea and 2.3mm for the esophagus on a database of 23 volumetric cardiovascular CT images. Furthermore, we show by quantitative evaluation that our integrated framework outperforms individual esophagus segmentation, and individual trachea segmentation if the trachea is only partly inside the field-of-view.

Proceedings Article | 27 March 2009 Paper

A multi-modality segmentation framework: application to fully automatic heart segmentation

Carsten Meyer, Olivier Ecabert, Jochen Peters, Reinhard Kneser, Robert Manzke, Raymond Chan, Jürgen Weese

Proceedings Volume 7259, 72594L (2009) https://doi.org/10.1117/12.810919

KEYWORDS: Image segmentation, 3D image processing, Magnetic resonance imaging, X-ray computed tomography, 3D modeling, Heart, X-rays, Target detection, Data modeling, Calibration

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Automatic segmentation is a prerequisite to efficiently analyze the large amount of image data produced by modern imaging modalities, e.g., computed tomography (CT), magnetic resonance (MR) and rotational X-ray volume imaging. While many segmentation approaches exist, most of them are developed for a single, specific imaging modality and a single organ. In clinical practice, however, it is becoming increasingly important to handle multiple modalities: First due to a case-specific choice of the most suitable imaging modality (e.g. CT versus MR), and second in order to integrate complementary data from multiple modalities. In this paper, we present a single, integrated segmentation framework which can easily be adapted to a range of imaging modalities and organs. Our algorithm is based on shape-constrained deformable models. Key elements are (1) a shape model representing the geometry and variability of the target organ of interest, (2) spatially varying boundary detection functions representing the gray value appearance of the organ boundaries for the specific imaging modality or protocol, and (3) a multi-stage segmentation approach. Focussing on fully automatic heart segmentation, we present evaluation results for CT,MR (contrast enhanced and non-contrasted), and rotational X-ray angiography (3-D RA). We achieved a mean segmentation error of about 0.8mm for CT and (non-contrasted) MR, 1.0mm for contrast-enhanced MR and 1.3mm for 3-D RA, demonstrating the success of our segmentation framework across modalities.

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