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Deep learning has been widely applied for left ventricle (LV) analysis, obtaining state of the art results in quantification through image segmentation. When the training datasets are limited, data augmentation becomes critical, but standard augmentation methods do not usually incorporate the natural variation of anato-my. In this paper we propose a pipeline for LV quantification applying our data augmentation methodology based on statistical models of deformations (SMOD) to quantify LV based on segmentation of cardiac MR (CMR) images, and present an in-depth analysis of the effects of deformation parameters in SMOD perfor-mance. We trained and evaluated our pipeline on the MICCAI 2019 Left Ventri-cle Full Quantification Challenge dataset, and achieved average mean absolute er-ror (MAE) for areas, dimensions, regional wall thickness and phase of 106mm2, 1.52mm, 1.01mm and 8.0% respectively in a 3-fold cross-validation experiment.


Conference paper



Publication Date



Deep Learning, Data Augmentation, LV Quantification, Cardiac, MRI