AiAReSeg: Catheter Detection and Segmentation in Interventional Ultrasound using Transformers

TL;DR

This paper introduces AiAReSeg, a transformer-based deep learning model for detecting and segmenting catheters in ultrasound images, aiming to replace fluoroscopy and improve safety in endovascular surgeries.

Contribution

It adapts transformer architectures for ultrasound catheter segmentation and introduces a novel 3D segmentation head and a physics-based data synthesis pipeline.

Findings

Demonstrates robustness to ultrasound noise and varying angles

Validated on phantom data showing potential for clinical translation

Achieves accurate catheter detection and segmentation in ultrasound sequences

Abstract

To date, endovascular surgeries are performed using the golden standard of Fluoroscopy, which uses ionising radiation to visualise catheters and vasculature. Prolonged Fluoroscopic exposure is harmful for the patient and the clinician, and may lead to severe post-operative sequlae such as the development of cancer. Meanwhile, the use of interventional Ultrasound has gained popularity, due to its well-known benefits of small spatial footprint, fast data acquisition, and higher tissue contrast images. However, ultrasound images are hard to interpret, and it is difficult to localise vessels, catheters, and guidewires within them. This work proposes a solution using an adaptation of a state-of-the-art machine learning transformer architecture to detect and segment catheters in axial interventional Ultrasound image sequences. The network architecture was inspired by the Attention in Attention mechanism, temporal tracking networks, and introduced a novel 3D segmentation head that performs 3D deconvolution across time. In order to facilitate training of such deep learning networks, we introduce a new data synthesis pipeline that used physics-based catheter insertion simulations, along with a convolutional ray-casting ultrasound simulator to produce synthetic ultrasound images of endovascular interventions. The proposed method is validated on a hold-out validation dataset, thus demonstrated robustness to ultrasound noise and a wide range of scanning angles. It was also tested on data collected from silicon-based aorta phantoms, thus demonstrated its potential for translation from sim-to-real. This work represents a significant step towards safer and more efficient endovascular surgery using interventional ultrasound.