Multi-Scale Deep Networks and Regression Forests for Direct Bi-ventricular Volume Estimation
We propose a general, fully learning-based framework for direct estimation of cardiac bi-ventricular volumes by combining the strengths of both generative and discriminant learning. A novel multi-scale convolutional deep network is proposed for unsupervised cardiac image representation learning from unlabeled data. Bi-ventricular volume estimation is formulated as a regression problem and random forests are employed for efficient volume estimation.
multi-scale deep networks regression random forests direct volume estimation