高密度显微数据中的快速样条检测
Fast spline detection in high density microscopy data
AI总结：
针对多生物显微数据中重叠细长体检测难的问题，提出端到端深度学习方法，可在低分辨率下快速追踪数千个重叠样条，基于合成数据训练并能泛化到真实线虫实验视频。
摘要：
借助通用深度学习技术，生物显微数据的计算机辅助分析已取得巨大进展。然而在多生物系统的显微研究中，碰撞与重叠问题仍具挑战性，对于由爬行线虫、游动精子或真核/原核鞭毛摆动等细长体构成的系统而言尤其如此。本文提出一种新型端到端深度学习方法，可提取通常处于运动状态且相互重叠的样条的精确形状轨迹。该方法适用于特征关键点难以定义与检测的低分辨率场景，检测速度快，可同时追踪数千个重叠的生物体。尽管该方法不局限于特定应用领域，本文仍以密集爬行的秀丽隐杆线虫（Caenorhabditis elegans）实验为场景，验证了其可用性。模型完全基于合成数据训练，采用了基于物理的线虫运动模型，且本文证明了模型具备从模拟数据泛化到实验视频的能力。
英文摘要：
Computer-aided analysis of biological microscopy data has seen a massive improvement with the utilization of general-purpose deep learning techniques. Yet, in microscopy studies of multi-organism systems, the problem of collision and overlap remains challenging. This is particularly true for systems composed of slender bodies such as crawling nematodes, swimming spermatozoa, or the beating of eukaryotic or prokaryotic flagella. Here, we develop a novel end-to-end deep learning approach to extract precise shape trajectories of generally motile and overlapping splines. Our method works in low resolution settings where feature keypoints are hard to define and detect. Detection is fast and we demonstrate the ability to track thousands of overlapping organisms simultaneously. While our approach is agnostic to area of application, we present it in the setting of and exemplify its usability on dense experiments of crawling Caenorhabditis elegans. The model training is achieved purely on synthetic data, utilizing a physics-based model for nematode motility, and we demonstrate the model's ability to generalize from simulations to experimental videos.
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