CathFlow: Self-Supervised Segmentation of Catheters in Interventional Ultrasound Using Optical Flow and Transformers

Liming Kuang

Abstract:

In minimally invasive endovascular procedures,
contrast-enhanced angiography remains the most robust imaging technique, but exposes patients and surgeons to prolonged
radiation. Alternatives such as ultrasound are difficult to
interpret, are highly prone to artifacts and noise, and vary
in quality, depending on the experience of the interventional
radiologist and machine settings. 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 mask 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.