Time-lapse microscopy movies have transformed the study of subcellular dynamics. However, manual analysis of movies can introduce bias and variability, obscuring important insights. While automation can overcome such limitations, spatial and temporal discontinuities in time-lapse movies render methods such as 3D object segmentation and tracking difficult. Here, we present SpinX, a framework for reconstructing gaps between successive image frames by combining deep learning and mathematical object modeling. By incorporating expert feedback through selective annotations, SpinX identifies subcellular structures, despite confounding neighbor-cell information, non-uniform illumination, and variable fluorophore marker intensities. The automation and continuity introduced here allows the precise 3D tracking and analysis of spindle movements with respect to the cell cortex for the first time. We demonstrate the utility of SpinX using distinct spindle markers, cell lines, microscopes, and drug treatments. In summary, SpinX provides an exciting opportunity to study spindle dynamics in a sophisticated way, creating a framework for step changes in studies using time-lapse microscopy.

中文翻译：

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深度学习技术和数学建模可对有丝分裂纺锤体动力学进行 3D 分析

延时显微镜电影改变了亚细胞动力学的研究。然而，对电影的手动分析可能会引入偏差和变异性，从而掩盖重要的见解。虽然自动化可以克服这些限制，但延时电影中的空间和时间不连续性使得 3D 对象分割和跟踪等方法变得困难。在这里，我们提出了 SpinX，这是一个通过结合深度学习和数学对象建模来重建连续图像帧之间间隙的框架。通过选择性注释结合专家反馈，SpinX 可以识别亚细胞结构，尽管存在混淆的邻近细胞信息、不均匀照明和可变的荧光团标记强度。这里引入的自动化和连续性首次允许对细胞皮层的主轴运动进行精确的 3D 跟踪和分析。我们使用不同的纺锤体标记、细胞系、显微镜和药物治疗来展示 SpinX 的实用性。总之，SpinX 提供了一个以复杂方式研究主轴动力学的令人兴奋的机会，为使用延时显微镜研究的逐步变化创建了一个框架。