Using deformable surfaces to segment 3-D images and infer differential structures
CVGIP: Image Understanding
Surface simplification using quadric error metrics
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
A parametric deformable model to fit unstructured 3D data
Computer Vision and Image Understanding
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
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The preoperative planning of primary tumor resections in the larynx region shall be supported by a 3D visualization of the patient-specific anatomy and pathological situation. This requires a segmentation of the larynx cartilage structures from computed tomography (CT) datasets. In our work, we use 3D Stable Mass-Spring Models (SMSMs) for this segmentation task. Thereto, we create a specific 3D deformable shape model for the segmentation of the thyroid cartilage. A new concept for elastic initialization of this model is presented, allowing the deformable model to adapt specifically to patient-specific shape variations and pathological deformations. We show that using our generation and initialization method, prototypical 3D deformable shape models can be adapted to very different patients without any prior training and prior knowledge about new patients' data.