Segmenting The Left Ventricle In Cardiac In Cardiac MRI: From Handcrafted To Deep Region Based Descriptors
Daniela O. Medley, Carlos Santiago, Jacinto C. Nascimento · 2019
Achieving robust object segmentations requires the ability to discard outliers (invalid observations) in the segmentation process. When considering an object model, such as an active shape model (ASM), described by a set of points, a one-to-one mapping from one model point to one valid observation would be ideal. However, in general, a one-to-many mapping is necessary to ensure the valid observation is detected and, thus, to have a reliable and robust fitting process. In this work, we compare three observation detectors: two of them based on handcrafted texture features and another based on a deep CNN classifier. Furthermore, to reduce from many detections to just one valid observation, we incorporate the Generalized Expectation-Maximization (GEM) algorithm in the ASM framework. This algorithm is able to neglect outliers by assigning them low weights. The proposed methodology exhibits remarkable accuracy with all the detectors in the context of the segmentation of the left ventricle in two publicly available MRI datasets, for which the proposed approach is competitive with other state-of-the-art methods.