TY - GEN
T1 - Unsupervised Deep Learning for the Matching of Vascular Anatomy in Multiple Digital Subtraction Angiograms
AU - Kraaijveld, R.C.J.
AU - van der Velden, B.H.M.
AU - Goldman, R.E.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - digital subtraction angiography (DSA)
KW - image interpretation
KW - image-guided procedures
KW - localization and tracking technology
KW - Unsupervised learning
U2 - 10.1117/12.3004394
DO - 10.1117/12.3004394
M3 - Conference paper
AN - SCOPUS:85192357955
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2024
A2 - Mello-Thoms, Claudia R.
A2 - Mello-Thoms, Claudia R.
A2 - Chen, Yan
PB - SPIE
T2 - Medical Imaging 2024: Image Perception, Observer Performance, and Technology Assessment
Y2 - 20 February 2024 through 22 February 2024
ER -