Segmenting cells and tracking their motion over time is a common task in biomedical applications. However, predicting accurate instance-wise segmentation and cell motions from microscopy imagery remains a challenging task. Using microstructured environments for analyzing single cells in a constant flow of media adds additional complexity. While large-scale labeled microscopy datasets are available, we are not aware of any large-scale dataset, including both cells and microstructures. In this paper, we introduce the trapped yeast cell (TYC) dataset, a novel dataset for understanding instance-level semantics and motions of cells in microstructures. We release $105$ dense annotated high-resolution brightfield microscopy images, including about $19$k instance masks. We also release $261$ curated video clips composed of $1293$ high-resolution microscopy images to facilitate unsupervised understanding of cell motions and morphology. TYC offers ten times more instance annotations than the previously largest dataset, including cells and microstructures. Our effort also exceeds previous attempts in terms of microstructure variability, resolution, complexity, and capturing device (microscopy) variability. We facilitate a unified comparison on our novel dataset by introducing a standardized evaluation strategy. TYC and evaluation code are publicly available under CC BY 4.0 license.

翻译：对细胞进行分割并追踪其随时间推移的运动轨迹是生物医学应用中的常见任务。然而，从显微图像中准确预测实例级分割和细胞运动仍是一项具有挑战性的任务。利用微结构环境在恒定介质流中分析单细胞进一步增加了复杂性。尽管已有大规模标注的显微数据集，但我们尚未发现任何同时包含细胞与微结构的大规模数据集。本文提出了一种新型数据集——捕获酵母细胞（TYC）数据集，用于理解微结构中细胞的实例级语义和运动。我们发布了105张密集标注的高分辨率明场显微图像，包含约1.9万个实例掩膜。此外，我们还发布了由1293张高分辨率显微图像组成的261个精选视频片段，以促进对细胞运动与形态的无监督理解。TYC提供的实例标注数量是此前包含细胞与微结构的最大数据集的十倍。我们的工作在微结构多样性、分辨率、复杂度及成像设备（显微镜）差异性方面均超越了以往尝试。通过引入标准化评估策略，我们在此新数据集上实现了统一的比较。TYC数据集及评估代码以CC BY 4.0许可协议公开提供。