Image-based estimation of the left ventricular cavity volume using deep learning and Gaussian process with cardio-mechanical applications

In this investigation, an image-based method has been developed to estimate the volume of the left ventricular cavity using cardiac magnetic resonance (CMR) imaging data. Deep learning and Gaussian processes have been applied to bring the estimations closer to the cavity volumes manually extracted. CMR data from 339 patients and healthy volunteers have been used to train a stepwise regression model that can estimate the volume of the left ventricular cavity at the beginning and end of diastole. We have decreased the root mean square error (RMSE) of cavity volume estimation approximately from 13 to 8 ml compared to the common practice in the literature. Considering the RMSE of manual measurements is approximately 4 ml on the same dataset, 8 ml of error is notable for a fully automated estimation method, which needs no supervision or user-hours once it has been trained. Additionally, to demonstrate a clinically relevant application of automatically estimated volumes, we inferred the passive material properties of the myocardium given the volume estimates using a well-validated cardiac model. These material properties can be further used for patient treatment planning and diagnosis.

Automated summary

An image-based method using cardiac magnetic resonance (CMR) imaging data was developed to estimate the volume of the left ventricular cavity. Deep learning and Gaussian processes were employed to improve the accuracy of estimations compared to manual extraction. A stepwise regression model trained on CMR data from 339 patients and healthy volunteers was able to estimate the volume of the left ventricular cavity during diastole. The method achieved a root mean square error (RMSE) of approximately 8 ml, which is notable considering the automated nature of the estimation.