Tracking single molecules is instrumental for quantifying the transport of
molecules and nanoparticles in biological samples, e.g., in brain drug delivery
studies. Existing intensity-based localisation methods are not developed for
imaging with a scanning microscope, typically used for in vivo imaging. Low
signal-to-noise ratios, movement of molecules out-of-focus, and high motion
blur on images recorded with scanning two-photon microscopy (2PM) in vivo pose
a challenge to the accurate localisation of molecules. Using data-driven models
is challenging due to low data volumes, typical for in vivo experiments. We
developed a 2PM image simulator to supplement scarce training data. The
simulator mimics realistic motion blur, background fluorescence, and shot noise
observed in vivo imaging. Training a data-driven model with simulated data
improves localisation quality in simulated images and shows why intensity-based
methods fail.

本研究通过开发 2PM 图像模拟器来解决药物输送研究中单分子追踪的困难问题，为提高定位精度，提出了基于数据驱动模型的训练方法，并阐述了传统强度基础定位方法的不足。

基于深度学习的合成双光子显微图像中纳米粒子定位

Deep Learning-Assisted Localisation of Nanoparticles in synthetically generated two-photon microscopy images

Powered by acoustics, existing therapeutic and diagnostic procedures will
become less invasive and new methods will become available that have never been
available before. Acoustically driven microrobot navigation based on
microbubbles is a promising approach for targeted drug delivery. Previous
studies have used acoustic techniques to manipulate microbubbles in vitro and
in vivo for the delivery of drugs using minimally invasive procedures. Even
though many advanced capabilities and sophisticated control have been achieved
for acoustically powered microrobots, there remain many challenges that remain
to be solved. In order to develop the next generation of intelligent
micro/nanorobots, it is highly desirable to conduct accurate identification of
the micro-nanorobots and to control their dynamic motion autonomously. Here we
use reinforcement learning control strategies to learn the microrobot dynamics
and manipulate them through acoustic forces. The result demonstrated for the
first time autonomous acoustic navigation of microbubbles in a microfluidic
environment. Taking advantage of the benefit of the second radiation force,
microbubbles swarm to form a large swarm, which is then driven along the
desired trajectory. More than 100 thousand images were used for the training to
study the unexpected dynamics of microbubbles. As a result of this work, the
microrobots are validated to be controlled, illustrating a good level of
robustness and providing computational intelligence to the microrobots, which
enables them to navigate independently in an unstructured environment without
requiring outside assistance.

利用强化学习控制策略，将声学力学用于微型机器人的导航，通过第二辐射力及微泡聚集优势，成功地实现了微泡在微流体环境下的自主声学导航，为制造具备计算智能并能在非结构化环境中独立导航的智能微纳机器人提供了基础支持。