Motion Enhanced Multi‐Level Tracker (MEMTrack): A Deep Learning‐Based Approach to Microrobot Tracking in Dense and Low‐Contrast Environments
Motion Enhanced Multi‐Level Tracker (MEMTrack): A Deep Learning‐Based Approach to Microrobot Tracking in Dense and Low‐Contrast Environments
复制标题
运动增强型多级跟踪器 (MEMTrack)：一种基于深度学习的方法，用于在密集和低对比度环境中跟踪微型机器人
DOI：
10.1002/aisy.202300590
复制
发表时间：
2024
影响因子：
7.4
通讯作者：
Behkam, Bahareh
中科院分区：
文献类型：
--
作者：
Sawhney, Medha;Karmarkar, Bhas;Leaman, Eric J.;Daw, Arka;Karpatne, Anuj;Behkam, Bahareh
跟踪微型机器人是具有挑战性的，因为它们的微小尺寸和高速。在生物医学应用中，这种挑战因周围环境密集而加剧，其特征尺寸和形状与微型机器人相当。在此，运动增强多级跟踪器（MEMTrack）被引入用于在密集和低对比度环境中检测和跟踪微型机器人。根据微型机器人运动的物理特性，基于深度学习的物体检测的合成运动特征和带有插值的修改后的简单在线和真实的实时跟踪（SORT）算法用于跟踪。MEMTrack使用胶原蛋白（组织模型）中的细菌微马达进行训练和测试，分别达到76%和51%的精确度和召回率。与最先进的基线模型相比，MEMTrack提供了至少2.6倍的高精度和相当高的召回率。MEMTrack的概括性看不见的（水）介质和它的多功能性，在跟踪不同形状，大小和运动特性的微型机器人。最后，MEMTrack定位目标的均方根误差小于1.84 μm，并量化所有测试系统的平均速度，与费力生成的手动跟踪数据没有统计学显著差异。MEMTrack在密集和低对比度环境中显着推进微型机器人定位和跟踪，并可以影响基础和转化微型机器人研究。
Tracking microrobots is challenging due to their minute size and high speed. In biomedical applications, this challenge is exacerbated by the dense surrounding environments with feature sizes and shapes comparable to microrobots. Herein, Motion Enhanced Multi‐level Tracker (MEMTrack) is introduced for detecting and tracking microrobots in dense and low‐contrast environments. Informed by the physics of microrobot motion, synthetic motion features for deep learning‐based object detection and a modified Simple Online and Real‐time Tracking (SORT)algorithm with interpolation are used for tracking. MEMTrack is trained and tested using bacterial micromotors in collagen (tissue phantom), achieving precision and recall of 76% and 51%, respectively. Compared to the state‐of‐the‐art baseline models, MEMTrack provides a minimum of 2.6‐fold higher precision with a reasonably high recall. MEMTrack's generalizability to unseen (aqueous) media and its versatility in tracking microrobots of different shapes, sizes, and motion characteristics are shown. Finally, it is shown that MEMTrack localizes objects with a root‐mean‐square error of less than 1.84 μm and quantifies the average speed of all tested systems with no statistically significant difference from the laboriously produced manual tracking data. MEMTrack significantly advances microrobot localization and tracking in dense and low‐contrast settings and can impact fundamental and translational microrobotic research.