Real-time segmentation by Active Geometric Functions

Authors:
Qi Duan;Elsa D. Angelini;Andrew F. Laine
Affiliations:
Department of Biomedical Engineering, Columbia University, ET-351, 1210 Amsterdam Avenue, New York, NY 10027, USA and Department of Radiology, NYU School of Medicine, New York, NY, USA;Department of Image and Signal Processing, Institut Telecom, Telecom ParisTech, CNRS LTCI, France;Department of Biomedical Engineering, Columbia University, ET-351, 1210 Amsterdam Avenue, New York, NY 10027, USA
Venue:
Computer Methods and Programs in Biomedicine
Year:
2010

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Abstract

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.