2 research outputs found
AiAReSeg: Catheter Detection and Segmentation in Interventional Ultrasound using Transformers
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.Comment: This work has been submitted to the IEEE for possible publicatio
Identifying Visible Tissue in Intraoperative Ultrasound Images during Brain Surgery: A Method and Application
Intraoperative ultrasound scanning is a demanding visuotactile task. It
requires operators to simultaneously localise the ultrasound perspective and
manually perform slight adjustments to the pose of the probe, making sure not
to apply excessive force or breaking contact with the tissue, whilst also
characterising the visible tissue. In this paper, we propose a method for the
identification of the visible tissue, which enables the analysis of ultrasound
probe and tissue contact via the detection of acoustic shadow and construction
of confidence maps of the perceptual salience. Detailed validation with both in
vivo and phantom data is performed. First, we show that our technique is
capable of achieving state of the art acoustic shadow scan line classification
- with an average binary classification accuracy on unseen data of 0.87.
Second, we show that our framework for constructing confidence maps is able to
produce an ideal response to a probe's pose that is being oriented in and out
of optimality - achieving an average RMSE across five scans of 0.174. The
performance evaluation justifies the potential clinical value of the method
which can be used both to assist clinical training and optimise robot-assisted
ultrasound tissue scanning