Automated chromosome instance segmentation from metaphase cell microscopic images is critical for the diagnosis of chromosomal disorders (i.e., karyotype analysis). However, it is still a challenging task due to lacking of densely annotated datasets and the complicated morphologies of chromosomes, e.g., dense distribution, arbitrary orientations, and wide range of lengths. To facilitate the development of this area, we take a big step forward and manually construct a large-scale densely annotated dataset named AutoKary2022, which contains over 27,000 chromosome instances in 612 microscopic images from 50 patients. Specifically, each instance is annotated with a polygonal mask and a class label to assist in precise chromosome detection and segmentation. On top of it, we systematically investigate representative methods on this dataset and obtain a number of interesting findings, which helps us have a deeper understanding of the fundamental problems in chromosome instance segmentation. We hope this dataset could advance research towards medical understanding. The dataset can be available at: https://github.com/wangjuncongyu/chromosome-instance-segmentation-dataset.

翻译：从中期细胞显微图像中自动进行染色体实例分割对于染色体疾病诊断（如核型分析）至关重要。然而，由于缺乏密集标注的数据集以及染色体形态复杂（例如密集分布、任意朝向及长度范围广），该任务仍面临挑战。为推动该领域发展，我们迈出重要一步，人工构建了名为AutoKary2022的大规模密集标注数据集，该数据集包含来自50位患者的612张显微图像中的超过27,000个染色体实例。具体而言，每个实例均附带多边形掩膜与类别标签，以支持精确的染色体检测与分割。基于此数据集，我们系统研究了代表性方法，并获得多项有趣发现，这有助于更深入理解染色体实例分割中的基本问题。希望该数据集能推动医学理解相关研究。数据集获取地址：https://github.com/wangjuncongyu/chromosome-instance-segmentation-dataset。