Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning

Fig 1. The deep learning network architecture. Each red block represents two consecutive sets of 3x3 Convolution layer, ReLU activation and Batch Normalization. The number of feature maps in each layer is on top of each box, while the size of each feature map per layer is indicated on the left. Finally, the feature maps are passed through a softmax layer to obtain pixel wise probabilities of belonging to the given class to obtain the outputs.

Fig 2. Dice score visualization. The Dice score is used to gauge model performance, ranging from 0 to 1. 1 corresponds to a pixel perfect match between the deep learning model output (red, A and D) and ground truth annotation (green, B and D). The model output with the higher Dice score (A) had greater overlap (C) with the ground truth (B) than the output (D) that had lesser overlap (F), as it did not predict that main pulmonary artery that was annotated in (E).

Fig 3. Demonstrative comparisons between the ground truth manual annotation (green) and prediction overlaid on the image(red) are shown for the: A) PAA, B) DA, C) SVC, D) IVC, E) CS, F) PA, G) RVW and H) LAW.

Fig 4. Accurate prediction of the SVC despite suboptimal contrast opacification. The ground truth annotation is in green and the model prediction in red. Abbreviations: SVC = superior vena cava.

Fig 5. An example of the model as a “second reader”, correcting human error. The DA was partially missed during that were missed during manual annotation (green). The model correctly identified these (red), but obtained a lower Dice score as it did not match the ground truth set during manual annotation. Abbreviations: DA = descending aorta.