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.

提出了一种使用改进的机器学习变压器架构来检测和分割超声辅助介入导管的方法，借助物理建模和超声模拟器产生合成超声图像，并经实验证明了其在超声噪声和扫描角度范围上的鲁棒性，为更安全高效的血管内手术迈出了重要的一步。

AiAReSeg: 利用 Transformer 进行介入超声波的导管检测与分割

AiAReSeg: Catheter Detection and Segmentation in Interventional  Ultrasound using Transformers

Fluoroscopy is the standard imaging modality used to guide hip surgery and is
therefore a natural sensor for computer-assisted navigation. In order to
efficiently solve the complex registration problems presented during
navigation, human-assisted annotations of the intraoperative image are
typically required. This manual initialization interferes with the surgical
workflow and diminishes any advantages gained from navigation. We propose a
method for fully automatic registration using annotations produced by a neural
network. Neural networks are trained to simultaneously segment anatomy and
identify landmarks in fluoroscopy. Training data is obtained using an
intraoperatively incompatible 2D/3D registration of hip anatomy. Ground truth
2D labels are established using projected 3D annotations. Intraoperative
registration couples an intensity-based strategy with annotations inferred by
the network and requires no human assistance. Ground truth labels were obtained
in 366 fluoroscopic images across 6 cadaveric specimens. In a
leave-one-subject-out experiment, networks obtained mean dice coefficients for
left and right hemipelves, left and right femurs of 0.86, 0.87, 0.90, and 0.84.
The mean 2D landmark error was 5.0 mm. The pelvis was registered within 1
degree for 86% of the images when using the proposed intraoperative approach
with an average runtime of 7 seconds. In comparison, an intensity-only approach
without manual initialization, registered the pelvis to 1 degree in 18% of
images. We have created the first accurately annotated, non-synthetic, dataset
of hip fluoroscopy. By using these annotations as training data for neural
networks, state of the art performance in fluoroscopic segmentation and
landmark localization was achieved. Integrating these annotations allows for a
robust, fully automatic, and efficient intraoperative registration during
fluoroscopic navigation of the hip.

这篇论文提出了一种使用神经网络技术和自动标记方法，在髋部手术中实现完全自动注册和识别标记，从而提高手术效率和精度。

用于 2D/3D 配准的髋部 X 线解剖结构自动标注的高效鲁棒性方法

Automatic Annotation of Hip Anatomy in Fluoroscopy for Robust and  Efficient 2D/3D Registration

Accurate and consistent mental interpretation of fluoroscopy to determine the
position and orientation of acetabular bone fragments in 3D space is difficult.
We propose a computer assisted approach that uses a single fluoroscopic view
and quickly reports the pose of an acetabular fragment without any user input
or initialization. Intraoperatively, but prior to any osteotomies, two
constellations of metallic ball-bearings (BBs) are injected into the wing of a
patient's ilium and lateral superior pubic ramus. One constellation is located
on the expected acetabular fragment, and the other is located on the remaining,
larger, pelvis fragment. The 3D locations of each BB are reconstructed using
three fluoroscopic views and 2D/3D registrations to a preoperative CT scan of
the pelvis. The relative pose of the fragment is established by estimating the
movement of the two BB constellations using a single fluoroscopic view taken
after osteotomy and fragment relocation. BB detection and inter-view
correspondences are automatically computed throughout the processing pipeline.
The proposed method was evaluated on a multitude of fluoroscopic images
collected from six cadaveric surgeries performed bilaterally on three
specimens. Mean fragment rotation error was 2.4 +/- 1.0 degrees, mean
translation error was 2.1 +/- 0.6 mm, and mean 3D lateral center edge angle
error was 1.0 +/- 0.5 degrees. The average runtime of the single-view pose
estimation was 0.7 +/- 0.2 seconds. The proposed method demonstrates accuracy
similar to other state of the art systems which require optical tracking
systems or multiple-view 2D/3D registrations with manual input. The errors
reported on fragment poses and lateral center edge angles are within the
margins required for accurate intraoperative evaluation of femoral head
coverage.

提出了一种计算机辅助方案，可使用单个荧光镜视图快速报告髋臼骨折或缺损的姿态而无需用户输入或初始化，该方案在多个尸检手术中进行了验证。