Real-time segmentation by Active Geometric Functions.
Recent advances in 4D imaging and real-time imaging provide image data with clinically important cardiac dynamic information at high spatial or temporal resolution. However, the enormous amount of information contained in these data has also raised a challenge for traditional image analysis algorithms in terms of efficiency. In this paper, a novel deformable model framework, Active Geometric Functions (AGF), is introduced to tackle the real-time segmentation problem. As an implicit framework paralleling to level-set, AGF has mathematical advantages in efficiency and computational complexity as well as several flexible feature similar to level-set framework. AGF is demonstrated in two cardiac applications: endocardial segmentation in 4D ultrasound and myocardial segmentation in MRI with super high temporal resolution. In both applications, AGF can perform real-time segmentation in several milliseconds per frame, which was less than the acquisition time per frame. Segmentation results are compared to manual tracing with comparable performance with inter-observer variability. The ability of such real-time segmentation will not only facilitate the diagnoses and workflow, but also enables novel applications such as interventional guidance and interactive image acquisition with online segmentation.
Duan
Qi
Q
Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA. Qi.Duan@nyumc.org
Angelini
Elsa D
ED
Laine
Andrew F
AF
D000465:Algorithms; D019561:Echocardiography, Four-Dimensional; D006321:Heart / Q000502:physiology; D006801:Humans; D007091:Image Processing, Computer-Assisted; D010363:Pattern Recognition, Automated; D013499:Surface Properties; D014584:User-Computer Interface