Manual segmentation and volumetric assessment

Pixel-wise manual segmentations of the LV and LA blood volumes, LV_m and LA_m respectively, (see Figure 1) were confirmed by an image analysis expert (author D.M.V.) with 7 years of experience in cardiac image segmentation using ITK-SNAP (Philadelphia, PA, USA).26 From each segmentation, blood chamber volumes were obtained, and the function of each chamber was measured via EF (LVEF_m and LAEF_m, respectively).

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 t→_DL, x→_DL, and 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. t→ is the displacement between the blue and black dot and x→and 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.