Title: Direct Multitype Cardiac Indices Estimation via Joint Representation and Regression Learning,
Abstract: Cardiac indices estimation is of great importance during identification and
diagnosis of cardiac disease in clinical routine. However, estimation of
multitype cardiac indices with consistently reliable and high accuracy is still
a great challenge due to the high variability of cardiac structures and
complexity of temporal dynamics in cardiac MR sequences. While efforts have
been devoted into cardiac volumes estimation through feature engineering
followed by a independent regression model, these methods suffer from the
vulnerable feature representation and incompatible regression model. In this
paper, we propose a semi-automated method for multitype cardiac indices
estimation. After manual labelling of two landmarks for ROI cropping, an
integrated deep neural network Indices-Net is designed to jointly learn the
representation and regression models. It comprises two tightly-coupled
networks: a deep convolution autoencoder (DCAE) for cardiac image
representation, and a multiple output convolution neural network (CNN) for
indices regression. Joint learning of the two networks effectively enhances the
expressiveness of image representation with respect to cardiac indices, and the
compatibility between image representation and indices regression, thus leading
to accurate and reliable estimations for all the cardiac indices.
When applied with five-fold cross validation on MR images of 145 subjects,
Indices-Net achieves consistently low estimation error for LV wall thicknesses
(1.44$\pm$0.71mm) and areas of cavity and myocardium (204$\pm$133mm$^2$). It
outperforms, with significant error reductions, segmentation method (55.1% and
17.4%) and two-phase direct volume-only methods (12.7% and 14.6%) for wall
thicknesses and areas, respectively. These advantages endow the proposed method
a great potential in clinical cardiac function assessment.