标题： 胶体显微镜：使用深度学习在三维中检测密集胶体 显示英文标题

标题： Colloidoscope: Detecting Dense Colloids in 3d with Deep Learning

摘要： Colloidoscope是一个深度学习流程，采用3D残差Unet架构，旨在通过共聚焦显微镜增强密集胶体悬浮液的跟踪。 该方法使用模拟训练数据集，反映各种现实成像条件，特别针对高胶体体积分数和低对比度的情况，传统检测方法在此情况下表现不佳。 我们方法的核心是使用实验信噪比（SNR）、对比噪声比（CNR）和点扩散函数（PSFs）来准确量化和模拟实验数据。 我们的研究结果表明，与传统启发式方法相比，Colloidoscope在粒子检测中具有更高的召回率（发现更多粒子）。 同时保持了高精度（真正例的高比例）。 该模型对光漂白样品表现出显著的鲁棒性，从而延长了成像时间和帧数。 此外，Colloidoscope保持了足够的小尺度分辨率，以分类局部结构特征。 在模拟和实验数据集上进行评估，Colloidoscope将深度学习在计算机视觉方面的进展带到了高体积分数下的粒子跟踪。 我们为软物质领域的研究人员提供了一个有前景的工具，该模型已部署并可供使用预训练版本：https://github.com/wahabk/colloidoscope。 显示英文摘要

摘要： Colloidoscope is a deep learning pipeline employing a 3D residual Unet architecture, designed to enhance the tracking of dense colloidal suspensions through confocal microscopy. This methodology uses a simulated training dataset that reflects a wide array of real-world imaging conditions, specifically targeting high colloid volume fraction and low-contrast scenarios where traditional detection methods struggle. Central to our approach is the use of experimental signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and point-spread-functions (PSFs) to accurately quantify and simulate the experimental data. Our findings reveal that Colloidoscope achieves superior recall in particle detection (finds more particles) compared to conventional heuristic methods. Simultaneously, high precision is maintained (high fraction of true positives.) The model demonstrates a notable robustness to photobleached samples, thereby prolonging the imaging time and number of frames than may be acquired. Furthermore, Colloidoscope maintains small scale resolution sufficient to classify local structural motifs. Evaluated across both simulated and experimental datasets, Colloidoscope brings the advancements in computer vision offered by deep learning to particle tracking at high volume fractions. We offer a promising tool for researchers in the soft matter community, this model is deployed and available to use pretrained: https://github.com/wahabk/colloidoscope.