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

Automatic segmentation is a prerequisite to efficiently analyze the large amount of image data produced by modem 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, 1.0mum for contrast-enhanced MR and 1.3mm for 3-D RA, demonstrating the success of our segmentation framework across modalities.