A GPU level-set segmentation framework for 3D Echocardiography

Real-time 3D Echocardiography (RT3DE), providing truly volumetric images of the heart, is a promising imaging modality for cardiac morphology and function assessment. Fast and automatic segmentation of the left ventricle (LV) is essential to enable an efficient quantitative analysis of these 3D data. Recently, we proposed a GPU implementation of the Level- Set (LS) Sparse Field algorithm for 3D image segmentation which combines high computational efficiency and flexibility in the interface evolution. In this work, we make our GPU LS solver able to deal with strongly inhomogeneous images such as the myocardial wall in RT3DE. The applicability of our method in clinical environment was evaluated by measuring LV volumetric parameters on 23 RT3DE exams, and comparing them with reference values from manual contouring. Results show the effectiveness of our framework in performing accurate LV myocardium segmentation in RT3DE near real-time.