Direct and simultaneous estimation of cardiac four chamber volumes by multioutput sparse regression

HIGHLIGHTSWe achieve direct and simultaneous four chamber volume estimation, which enables more accurate and convenient whole heart functional analysis.We formulate volume estimation as a multi‐output regression problem, which enables volume estimation to be conducted in a unified framework.We propose direct and simultaneous four chamber volume estimation by multioutput sparse regression.We have conducted extensive experiments on both MR and CT datasets for direct and simultaneous cardiac four‐chamber volume estimation. ABSTRACT Cardiac four‐chamber volume estimation serves as a fundamental and crucial role in clinical quantitative analysis of whole heart functions. It is a challenging task due to the huge complexity of the four chambers including great appearance variations, huge shape deformation and interference between chambers. Direct estimation has recently emerged as an effective and convenient tool for cardiac ventricular volume estimation. However, existing direct estimation methods were specifically developed for one single ventricle, i.e., left ventricle (LV), or bi‐ventricles; they can not be directly used for four chamber volume estimation due to the great combinatorial variability and highly complex anatomical interdependency of the four chambers. In this paper, we propose a new, general framework for direct and simultaneous four chamber volume estimation. We have addressed two key issues, i.e., cardiac image representation and simultaneous four chamber volume estimation, which enables accurate and efficient four‐chamber volume estimation. We generate compact and discriminative image representations by supervised descriptor learning (SDL) which can remove irrelevant information and extract discriminative features. We propose direct and simultaneous four‐chamber volume estimation by the multioutput sparse latent regression (MSLR), which enables jointly modeling nonlinear input‐output relationships and capturing four‐chamber interdependence. The proposed method is highly generalized, independent of imaging modalities, which provides a general regression framework that can be extensively used for clinical data prediction to achieve automated diagnosis. Experiments on both MR and CT images show that our method achieves high performance with a correlation coefficient of up to 0.921 with ground truth obtained manually by human experts, which is clinically significant and enables more accurate, convenient and comprehensive assessment of cardiac functions.