Studying cell morphology changes in time is critical to understanding cell migration mechanisms. In this work, we present a deep learning-based workflow to segment cancer cells embedded in 3D collagen matrices and imaged with phase-contrast microscopy. Our approach uses transfer learning and recurrent convolutional long-short term memory units to exploit the temporal information from the past and provide a consistent segmentation result. Lastly, we propose a geometrical-characterization approach to studying cancer cell morphology. Our approach provides stable results in time, and it is robust to the different weight initialization or training data sampling. We introduce a new annotated dataset for 2D cell segmentation and tracking, and an open-source implementation to replicate the experiments or adapt them to new image processing problems.

翻译：研究时间细胞形态变化对于理解细胞迁移机制至关重要。在这项工作中,我们展示了基于深层学习的工作流程,用于3D科伦基质中嵌入的癌细胞的分部,并以相光显微镜制成。我们的方法是转移学习和反复变换的长期短期记忆单元,以利用过去的时间信息,并提供一致的分解结果。最后,我们提出了研究癌症细胞形态的几何特征分析方法。我们的方法在时间上提供了稳定的结果,并且对不同的权重初始化或培训数据取样具有很强的力度。我们引入了一个新的2D细胞分解和跟踪附加说明数据集,以及开放源码的实施,以复制实验或使其适应新的图像处理问题。