Standard view cardiac computed tomography (CT) images are of great importance in clinical practice due to the valuable diagnostic information they provide. However, navigating these (3D) medical images to identify such a view can represent a time-consuming process, subject to inter-operator variability. This study presents an approach using a Multiscale Vision Transformer (MViT) to infer the rotation parameters required to obtain standard views and subsequently focus on extracting the left ventricular outflow tract (LVOT) view. Once this view is obtained, the aortic valve is detected and segmented using well-known convolutional neural networks (CNNs), specifically ResNet18 for detection and U-Net for segmentation. The methodology used for its training is detailed, along with the metrics employed to evaluate their performance. Finally, the results obtained for each stage are presented. This approach provides a clear understanding of the performance of each model, from rotation parameter prediction to aortic valve detection and segmentation.