Marching cubes: A high resolution 3D surface construction algorithm
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Active shape models—their training and application
Computer Vision and Image Understanding
Robust atlas-based segmentation of highly variable anatomy: left atrium segmentation
STACOM'10/CESC'10 Proceedings of the First international conference on Statistical atlases and computational models of the heart, and international conference on Cardiac electrophysiological simulation challenge
STACOM'10/CESC'10 Proceedings of the First international conference on Statistical atlases and computational models of the heart, and international conference on Cardiac electrophysiological simulation challenge
Automatic left atrium segmentation by cutting the blood pool at narrowings
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Principal spine shape deformation modes using riemannian geometry and articulated models
AMDO'06 Proceedings of the 4th international conference on Articulated Motion and Deformable Objects
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Automatic multi-model-based segmentation of the left atrium in cardiac MRI scans
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
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As a minimally invasive surgery to treat left atrial (LA) fibrillation, catheter based ablation uses high radio-frequency energy to eliminate potential sources of the abnormal electrical events, especially around the ostia of pulmonary veins (PV). Due to large structural variations of the PV drainage pattern, a personalized LA model is helpful to translate a generic ablation strategy to a specific patient's anatomy. Overlaying the LA model onto 2D fluoroscopic images provides valuable visual guidance during surgery. A holistic shape model is not accurate enough to represent the whole shape population of the LA. In this paper, we propose a part based LA model (including the chamber, appendage, and four major PVs) and each part is a much simpler anatomical structure compared to the holistic one. Our approach works on un-gated C-arm CT, where thin boundaries between the LA blood pool and surrounding tissues are often blurred due to the cardiac motion artifacts (which presents a big challenge compared to the highly contrasted gated CT/MRI). To avoid segmentation leakage, the shape prior is exploited in a model based approach to segment the LA parts. However, independent detection of each part is not optimal and its robustness needs further improvement (especially for the appendage and PVs). We propose to enforce a statistical shape constraint during the estimation of pose parameters (position, orientation, and size) of different parts. Our approach is computationally efficient, taking about 1.5 s to process a volume with 256×256×250 voxels. Experiments on 469 C-arm CT datasets demonstrate its robustness.