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使用深度学习技术追踪单细胞

Kristina Ulicna在人工智能显微镜研讨会上的视频演示

[Translate to chinese:] Separation of cells based on their tracking status: A colourised binary mask of a time-lapse microscopy field of view of medium confluency with individual cells highlighted as survivors if they can be tracked since the initial movie frame (cyan), incomers if they migrated into the field of view throughout the movie (yellow) or mistracks if an error occurred in the automated trajectory reconstruction (red). Tracking_single_cells_using_deep_learning_teaser.jpg

人工智能解决方案在显微镜领域的应用不断拓展。从自动化目标分类到虚拟染色,机器学习和深度学习技术在帮助显微镜学家简化分析工作的同时,也在持续推动科学技术领域的突破。

图像:根据追踪状态分离细胞:镜下观察培养基融合过程,其中单细胞被二值化遮罩标记为多色伪彩。如果细胞从初始帧开始便被追踪到,将被青色高亮标记为“幸存者”;如果在观察过程中进入视野,被黄色高亮标记为“外来者”,如果自动轨迹重建中出现错误,将被红色高亮显示为“失去跟踪”。

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