Segmentation of the left ventricle in cardiac MRI using a probabilistic data association active shape model

The 3D segmentation of endocardium of the left ventricle (LV) in cardiac MRI volumes is a challenging problem due to the intrinsic properties of this image modality. Typically, the object shape and position are estimated to fit the observed features collected from the images. The difficulty inherent to the LV segmentation in MRI is that the images contain outliers (i.e., observations not belonging to the LV border) due to the presence of other structures. This paper proposes a robust approach based on the Active Shape Model (ASM) that is able to circumvent the above problem. More specifically, the ASM will be guided by probabilistic data association filtering (PDAF) of strokes (i.e. line segments) computed in the neighborhood of the shape model. Thus, the proposed approach, termed herein as ASM-PDAF, will perform the following main steps: 1) edge detection (low-level features) in the vicinity of the shape model; 2) edge grouping (mid-level features) to obtain potential LV strokes; and 3) filtering using a PDAF framework (high-level features) to update the ASM. Experimental results on a public cardiac MRI database show that the proposed approach outperforms previous literature research.