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.
Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA. Qi.Duan@nyumc.org
Qi
Q
Duan
Elsa D
ED
Angelini
Andrew F
AF
Laine
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