Figure 1.

Deep learning model training approach and model architecture. (A) 3D computed tomography volumes were first resampled to uniform spatial resolution (1.5 mm isotopically) and uniform dimension (160 × 160 × 96) and then served as an input to all models. Step 1: Model-S was trained to predicted LV_DL (red) and LA_DL (green). Step 2: Model-T and Model-D were initialized by Model-S and then trained to predict imaging plane vectors $\overrightarrow{t}$_DL, $\overrightarrow{x}$_DL, and $\overrightarrow{y}$_DL. A graphic illustration of these three vectors in relationship to the image volume is shown. The blue cube represents the computed tomography volume with a re-sliced plane in black. The blue dot is the centre of volume and black dot is the centre of plane. $\overrightarrow{t}$ is the displacement between the blue and black dot and $\overrightarrow{x}$and $\overrightarrow{y}$ are directional vectors of the 2D plane in the volume’s coordinate system. (B) U-Net architecture with added branch consisting of four fully connected layers after the last max-pooling layer in the down-sampling path was used. Conv3D, 3D convolution layer.