  In minimally invasive endovascular procedures, contrast-enhanced angiography
remains the most robust imaging technique. However, it is at the expense of the
patient and clinician's health due to prolonged radiation exposure. As an
alternative, interventional ultrasound has notable benefits such as being
radiation-free, fast to deploy, and having a small footprint in the operating
room. Yet, ultrasound is hard to interpret, and highly prone to artifacts and
noise. Additionally, interventional radiologists must undergo extensive
training before they become qualified to diagnose and treat patients
effectively, leading to a shortage of staff, and a lack of open-source
datasets. In this work, we seek to address both problems by introducing a
self-supervised deep learning architecture to segment catheters in longitudinal
ultrasound images, without demanding any labeled data. The network architecture
builds upon AiAReSeg, a segmentation transformer built with the Attention in
Attention mechanism, and is capable of learning feature changes across time and
space. To facilitate training, we used synthetic ultrasound data based on
physics-driven catheter insertion simulations, and translated the data into a
unique CT-Ultrasound common domain, CACTUSS, to improve the segmentation
performance. We generated ground truth segmentation masks by computing the
optical flow between adjacent frames using FlowNet2, and performed thresholding
to obtain a binary map estimate. Finally, we validated our model on a test
dataset, consisting of unseen synthetic data and images collected from silicon
aorta phantoms, thus demonstrating its potential for applications to clinical
data in the future.
