Many medical ultrasound video recognition tasks involve identifying key
anatomical features regardless of when they appear in the video suggesting that
modeling such tasks may not benefit from temporal features. Correspondingly,
model architectures that exclude temporal features may have better sample
efficiency. We propose a novel multi-head attention architecture that
incorporates these hypotheses as inductive priors to achieve better sample
efficiency on common ultrasound tasks. We compare the performance of our
architecture to an efficient 3D CNN video recognition model in two settings:
one where we expect not to require temporal features and one where we do. In
the former setting, our model outperforms the 3D CNN - especially when we
artificially limit the training data. In the latter, the outcome reverses.
These results suggest that expressive time-independent models may be more
effective than state-of-the-art video recognition models for some common
ultrasound tasks in the low-data regime.

我们提出了一种新颖的多头注意力结构，将时间特征排除在外以实现在常见超声任务中更好的样本效率，结果表明，对于一些低数据量的常见超声任务，表达时间无关的模型可能比最先进的视频识别模型更有效。

医学超声视频识别中时间特征的相关性研究

On the Relevance of Temporal Features for Medical Ultrasound Video  Recognition

As medical ultrasound is becoming a prevailing examination approach nowadays,
robotic ultrasound systems can facilitate the scanning process and prevent
professional sonographers from repetitive and tedious work. Despite the recent
progress, it is still a challenge to enable robots to autonomously accomplish
the ultrasound examination, which is largely due to the lack of a proper task
representation method, and also an adaptation approach to generalize learned
skills across different patients. To solve these problems, we propose the
latent task representation and the robotic skills adaptation for autonomous
ultrasound in this paper. During the offline stage, the multimodal ultrasound
skills are merged and encapsulated into a low-dimensional probability model
through a fully self-supervised framework, which takes clinically demonstrated
ultrasound images, probe orientations, and contact forces into account. During
the online stage, the probability model will select and evaluate the optimal
prediction. For unstable singularities, the adaptive optimizer fine-tunes them
to near and stable predictions in high-confidence regions. Experimental results
show that the proposed approach can generate complex ultrasound strategies for
diverse populations and achieve significantly better quantitative results than
our previous method.

通过潜在任务表示和机器人技能适应性，该研究提出了自主超声的解决方案，以便实现机器人自动完成超声检查的挑战。该方法通过融合多模式超声技能并将其封装为低维概率模型来实现，实验结果显示其在不同人群中可以生成复杂的超声策略，并且相较于先前方法取得显著更好的定量结果。

自主超声学习：隐含任务表示与机器人技能适应

Learning Autonomous Ultrasound via Latent Task Representation and  Robotic Skills Adaptation

Medical ultrasound (US) is widely used to evaluate and stage vascular
diseases, in particular for the preliminary screening program, due to the
advantage of being radiation-free. However, automatic segmentation of small
tubular structures (e.g., the ulnar artery) from cross-sectional US images is
still challenging. To address this challenge, this paper proposes the DopUS-Net
and a vessel re-identification module that leverage the Doppler effect to
enhance the final segmentation result. Firstly, the DopUS-Net combines the
Doppler images with B-mode images to increase the segmentation accuracy and
robustness of small blood vessels. It incorporates two encoders to exploit the
maximum potential of the Doppler signal and recurrent neural network modules to
preserve sequential information. Input to the first encoder is a two-channel
duplex image representing the combination of the grey-scale Doppler and B-mode
images to ensure anatomical spatial correctness. The second encoder operates on
the pure Doppler images to provide a region proposal. Secondly, benefiting from
the Doppler signal, this work first introduces an online artery
re-identification module to qualitatively evaluate the real-time segmentation
results and automatically optimize the probe pose for enhanced Doppler images.
This quality-aware module enables the closed-loop control of robotic screening
to further improve the confidence and robustness of image segmentation. The
experimental results demonstrate that the proposed approach with the
re-identification process can significantly improve the accuracy and robustness
of the segmentation results (dice score: from 0:54 to 0:86; intersection over
union: from 0:47 to 0:78).

本文提出了一种利用 Doppler 效应的血管再识别模块的 DopUS-Net，用于血管病变的自动分割和血管筛选程序的实时监测，实验结果表明，这种方法可以显著提高分割结果的准确性和鲁棒性。