Learning-Based Shape Model for Coronary Artery CTA Analysis

The patent creates a learned shape model that copies and generalizes from manually annotated landmark positions in training images. This model can then automatically reproduce coronary artery structures in new images without requiring manual annotation for each case, thus maintaining diagnostic accuracy while dramatically reducing analysis time.

The patent performs preliminary action by training the shape model on a dataset of manually annotated images before actual diagnosis. The model learns the characteristic patterns and landmark positions during training, enabling it to automatically perform analysis on new patient images without requiring manual intervention during the diagnostic process.

The learned shape model copies the successful manual annotation process by internally storing the patterns and relationships learned from training data. When applied to new images, the model reproduces accurate coronary artery segmentations and landmark positions automatically, maintaining diagnostic accuracy while improving productivity by eliminating repetitive manual work.

The patent replaces the mechanical manual annotation process with an automated neural network-based system. The model substitutes human expertise with a computational system that can process images rapidly, maintaining the diagnostic accuracy achieved through manual analysis while dramatically improving analysis efficiency and productivity.

The patent segments the complex task of coronary artery analysis into distinct components: training phase (where the model learns from annotated data) and inference phase (where the model automatically analyzes new images). This segmentation allows the complex learning process to be encapsulated in the trained model, reducing the complexity burden during actual diagnostic use while maintaining time efficiency.

The learned shape model copies the complex patterns and relationships from training data into a compressed representation. During inference, the model simply needs to match these pre-learned patterns against new images, which is computationally much lighter than performing manual analysis. This copying approach reduces operational complexity while maintaining the accuracy achieved through extensive training.