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To date, endovascular surgeries are performed using the golden standard ofFluoroscopy, which uses ionising radiation to visualise catheters andvasculature. Prolonged Fluoroscopic exposure is harmful for the patient and theclinician, and may lead to severe post-operative sequlae such as thedevelopment of cancer. Meanwhile, the use of interventional Ultrasound hasgained popularity, due to its well-known benefits of small spatial footprint,fast data acquisition, and higher tissue contrast images. However, ultrasoundimages are hard to interpret, and it is difficult to localise vessels,catheters, and guidewires within them. This work proposes a solution using anadaptation of a state-of-the-art machine learning transformer architecture todetect and segment catheters in axial interventional Ultrasound imagesequences. The network architecture was inspired by the Attention in Attentionmechanism, temporal tracking networks, and introduced a novel 3D segmentationhead that performs 3D deconvolution across time. In order to facilitatetraining of such deep learning networks, we introduce a new data synthesispipeline that used physics-based catheter insertion simulations, along with aconvolutional ray-casting ultrasound simulator to produce synthetic ultrasoundimages of endovascular interventions. The proposed method is validated on ahold-out validation dataset, thus demonstrated robustness to ultrasound noiseand a wide range of scanning angles. It was also tested on data collected fromsilicon-based aorta phantoms, thus demonstrated its potential for translationfrom sim-to-real. This work represents a significant step towards safer andmore efficient endovascular surgery using interventional ultrasound.

AiAReSeg: Catheter Detection and Segmentation in Interventional Ultrasound using Transformers