Nascimento, J.Carneiro, G.2017-09-122017-09-122016Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging, 2016, vol.2016-June, pp.867-87197814799235021945-79281945-8452http://hdl.handle.net/2440/107659This paper proposes a novel combination of manifold learning with deep belief networks for the detection and segmentation of left ventricle (LV) in 2D - ultrasound (US) images. The main goal is to reduce both training and inference complexities while maintaining the segmentation accuracy of machine learning based methods for non-rigid segmentation methodologies. The manifold learning approach used can be viewed as an atlas-based segmentation. It partitions the data into several patches. Each patch proposes a segmentation of the LV that somehow must be fused. This is accomplished by a deep belief network (DBN) multi-classifier that assigns a weight for each patch LV segmentation. The approach is thus threefold: (i) it does not rely on a single segmentation, (ii) it provides a great reduction in the rigid detection phase that is performed at lower dimensional space comparing with the initial contour space, and (iii) DBN's allows for a training process that can produce robust appearance models without the need of large annotated training sets.en© 2016 IEEEManifolds, training, image segmentation, visualization, principal component analysis, complexity theory, contextConference paper003005160910.1109/ISBI.2016.74934030003863774002052-s2.0-84978394494260805Carneiro, G. [0000-0002-5571-6220]