# Automated Classification of Left Ventricular Hypertrophy on Cardiac MRI
## 2 Materials and Methods
### Model Architecture  
The algorithm decides whether the patient has hypertrophy. The input to the algorithm is created from CMR scans of 4 views (axis). We used multi-view data because hypertrophy classification is challenging and different views provide different information. It is possible to see a pattern on a short-axis image that cannot be seen on the long-axis images or the other way around. The input images were collected from 4 views:  
* Short-axis images from the apex to the base at different stages of the cardiac cycle;
* Long-axis, two-chamber images at different stages of the cardiac cycle (heart beat);
* Long-axis, three-chamber images at different stages of the cardiac cycle;
* Long-axis, four-chamber images at different stages of the cardiac cycle. The usage of all the images from the short-axis scan has difficulties. The input would be too big, and the number of images were not the same for all patients. Therefore, for the short-axis view, we took three images in each second phase of the cardiac cycle. At each chosen cardiac cycle, we used one image from the basal, one from the mid, and one from the apical region, resulting in 36 images; see Figures 1 and 2. In the case of the long-axis views, we took each second image from the cardiac cycle, resulting in 12 images; see Figure 3.  
The model is an ensemble of the extractors of the separate views. Images from each view are fed into a separate network to extract features. The features are concatenated, then the ensemble classifier is applied to obtain the prediction (normal or exhibiting hypertrophy); see Figure 4. The architecture of the extractor for the best-performing model can be seen in Figure 4, left side. We used the same extractor for each view. The extractors were trained separately; therefore, a temporary layer was applied to create a temporary classifier. The architecture of the temporary layer can be seen in Table 1. After the extractors are trained, an ensemble model is created with an ensemble classifier; see Figure 4, right side, under the classifier block. The models were built from residual blocks, with each block containing 3-dimensional convolutions and batch normalizations; see Figure 4, bottom part. Figure 1: The illustration of how the short-axis images are created. The systole is the phase where the heart volume is the lowest, while in the diastole, it is the highest. A heart beat (cardiac cycle) is divided into 25 phases. In each phase, several slices are scanned. See the parallel lines. There are three major regions for the heart: basal, mid, apical. Figure 2: A short-axis CMR scan produces around 400 images for a heart. There are 12–16 slices visited by the scanner at each phase (time point). During one heart beat (cardiac cycle), 25 images are created for a slice. There are three main regions: apical, mid, basal. See also Figure 1. To create a fixed-size input for the model, we picked three images from each second phase; see the gray boxes. Overall, this results in 36 images. Black squares show example images, how the real image looks at a given slice and phase. The reason for the 3D convolution is the positive effect of considering the time dimension of the input (how the heart moves). For further elaboration on the performance and the choices we made, see the details in Section 3.3. Figure 4: The schematic architecture of the model. Each view has an extractor and the output features from the extractors aggregated by concatenating the features along the channel dimension. This is the ensemble of the views. The extractor and the classifier are built from 3D residual blocks. The architecture of the 3D ResNet blocks can be seen in the middle of the image. The ResidualBlock and the ResBlockPooling differ in the strides. For the pooling block, the first convolution and the convolution on the skip branch have stride 2; otherwise, it is 1. The activations are ReLUs, which were applied after the batch normalization layers.