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

Carsten Meyer; Olivier Ecabert; Jochen Peters; Reinhard Kneser; Robert Manzke; Raymond C. Chan; Jürgen Weese

doi:10.1117/12.810919

27 March 2009 A multi-modality segmentation framework: application to fully automatic heart segmentation

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

Proceedings Volume 7259, Medical Imaging 2009: Image Processing; 72594L (2009) https://doi.org/10.1117/12.810919
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

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.

Citation Download Citation

Carsten Meyer, Olivier Ecabert, Jochen Peters, Reinhard Kneser, Robert Manzke, Raymond C. Chan, and Jürgen Weese "A multi-modality segmentation framework: application to fully automatic heart segmentation", Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 72594L (27 March 2009); https://doi.org/10.1117/12.810919

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