Key result
Novel probabilistic filter-based 3-D segmentation improves echocardiographic accuracy over state-of-the-art methods.
Why the study?
Accurate segmentation of the left ventricle in 3-D echocardiographic images is essential for assessing cardiac function but remains challenging.
May support automated 3D echo segmentation tools; leaves open larger prospective validation before clinical use.
The segmentation of the left ventricle (LV) is an important task to assess the cardiac function in ultrasound images of the heart. This paper presents a novel methodology for the segmentation of the LV in three-dimensional (3-D) echocardiographic images based on the probabilistic data association filter (PDAF). The proposed methodology begins by initializing a 3-D deformable model either semiautomatically, with user input, or automatically, and it comprises the following feature hierarchical approach: 1) edge detection in the vicinity of the surface (low-level features); 2) edge grouping to obtain potential LV surface patches (mid-level features); and 3) patch filtering using a shape-PDAF framework (high-level features). This method provides good performance accuracy in 20 echocardiographic volumes, and compares favorably with the state-of-the-art segmentation methodologies proposed in the recent literature.
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Santiago et al. (2014) studied Left ventricle segmentation (n=20). 3-D segmentation methodology based on probabilistic data association filter (PDAF) vs. State-of-the-art segmentation methodologies was evaluated on Performance accuracy. A novel 3-D segmentation methodology based on the probabilistic data association filter provided good performance accuracy in 20 echocardiographic volumes compared to state-of-the-art methods.
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