Key points are not available for this paper at this time.
Digital subtraction angiography (DSA) is commonly used in minimally invasive endovascular procedures for clinical decision-making in diagnosis and guidance. During a procedure, angiographic image sequences are taken sequentially, moving to more downstream blood vessels each time, mapping out the vascular structure of a patient. Localizing vascular structures within an image sequence with respect to prior image sequences can be challenging, especially when done in real-time. This study introduces a novel unsupervised method to localize DSA images with respect to each other in order to match the same vascular anatomy in different image sequences. The network consists of two parallel encoders that are used for matching and localization. First, images are matched according to the similarity of the encodings. Then, the encodings can be used to find the coordinate at which the images have the highest similarity, thus localizing the vasculature that matches in both images. The network was trained on a synthetic dataset which consisted of mother-daughter image pairs, where the daughter was a cropped version of a DSA image frame. The network was tested on a real-world dataset which consisted of image pairs that were matched according to anatomically neighboring blood vessels. Results show an AUC of 0.98 for the synthetic dataset and 0.69 for the real-world dataset. To conclude, the matching of the blood vessels was feasible with the use of unsupervised deep learning. The code can be found on: https://github.com/rooskraaijveld/ DSA localization.git
Kraaijveld et al. (Fri,) studied this question.