This study introduces a groundbreaking optical coherence tomography (OCT)
imaging system dedicated for high-throughput screening applications using ex
vivo tissue culture. Leveraging OCT's non-invasive, high-resolution
capabilities, the system is equipped with a custom-designed motorized platform
and tissue detection ability for automated, successive imaging across samples.
Transformer-based deep learning segmentation algorithms further ensure robust,
consistent, and efficient readouts meeting the standards for screening assays.
Validated using retinal explant cultures from a mouse model of retinal
degeneration, the system provides robust, rapid, reliable, unbiased, and
comprehensive readouts of tissue response to treatments. This fully automated
OCT-based system marks a significant advancement in tissue screening, promising
to transform drug discovery, as well as other relevant research fields.

本研究介绍了一种专用于高通量筛选应用的基于光学相干断层扫描（OCT）的影像系统，利用 OCT 非侵入性、高分辨率的能力，系统配备了一个定制的电动平台和组织检测功能，实现了样本之间自动、连续的成像。基于 Transformer 的深度学习分割算法进一步确保鲁棒、一致且高效的读数满足筛选分析的标准。通过使用小鼠视网膜退化模型的视网膜外植物培养进行验证，该系统提供了组织对治疗的反应的鲁棒、快速、可靠、无偏差和全面的读数。这种全自动 OCT 系统标志着组织筛选领域的重大进展，有望改变药物研发以及其他相关研究领域。