Accurate left atrium segmentation in multislice CT images using a shape model

Jens von Berg; Cristian Lorenz

doi:10.1117/12.595236

29 April 2005 Accurate left atrium segmentation in multislice CT images using a shape model

Jens von Berg, Cristian Lorenz

Proceedings Volume 5747, Medical Imaging 2005: Image Processing; (2005) https://doi.org/10.1117/12.595236
Event: Medical Imaging, 2005, San Diego, California, United States

Abstract

Segmentation and labelling of the left atrium from pre-operative images could be a valuable source of information for the planning of electrophysiology procedures to cure atrial fibrillation. A method is presented that uses multi-slice computed tomography (MSCT) images for this purpose that were initially acquired for coronary assessment. The method combines the power of active shape models (robustness by use of prior anatomical knowledge) with the advantages of solely data driven segmentation methods (accuracy). A triangular shape model was built for the human left atrium and its pulmonary vein trunks. It was automatically adapted to the MSCT images, labelling these structures and segmenting them coarsely. In addition, a segmentation of the blood pool by a Hounsfield threshold was applied to the images. The enclosed volumes were triangulated to get a fine surface representation yet still including many distracting objects (the artery tree, coronaries, adjacent chambers, and bones). A correspondence between surface triangles of the coarse, but anatomically labelled model surface and those of the fine iso-surface was established by a similarity criterion on position and orientation. This allows for the refinement of the model-based segmentation showing more anatomical details by selection of corresponding parts of the iso-surface. Vice versa, the correspondence could be used to assign anatomical labels to each iso-surface patch.

Citation Download Citation

Jens von Berg and Cristian Lorenz "Accurate left atrium segmentation in multislice CT images using a shape model", Proc. SPIE 5747, Medical Imaging 2005: Image Processing, (29 April 2005); https://doi.org/10.1117/12.595236

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