Northeast Forestry University, Mechanical and Electrical Engineering, Harbin 150006, China.
Harbin Institute of Technology, School of Computer Science, Harbin 150001, China.
Comput Methods Programs Biomed. 2023 Nov;241:107732. doi: 10.1016/j.cmpb.2023.107732. Epub 2023 Jul 28.
Nuclear segmentation in cervical cell images is a crucial technique for automatic cytopathology diagnosis. Experimental evaluation of nuclear segmentation methods with datasets is helpful in promoting the advancement of nuclear segmentation techniques. However, public datasets are not enough for a reasonable and comprehensive evaluation because of insufficient quantity, single data source, and low segmentation difficulty.
Therefore, we provide the largest dataset for cervical nuclear segmentation (CNSeg). It contains 124,000 annotated nuclei collected from 1,530 patients under different conditions. The image styles in this dataset cover most practical application scenarios, including microbial infection, cytopathic heterogeneity, overlapping nuclei, etc. To evaluate the performance of segmentation methods from different aspects, we divided the CNSeg dataset into three subsets, namely the patch segmentation dataset (PatchSeg) with nuclei images collected under complex conditions, the cluster segmentation dataset (ClusterSeg) with cluster nuclei, and the domain segmentation dataset (DomainSeg) with data from different domains. Furthermore, we propose a post-processing method that processes overlapping nuclei single ones.
Experiments show that our dataset can comprehensively evaluate cervical nuclear segmentation methods from different aspects. We provide guidelines for other researchers to use the dataset. https://github.com/jingzhaohlj/AL-Net.
在宫颈细胞图像中进行核分割是自动细胞病理学诊断的关键技术。使用数据集对核分割方法进行实验评估有助于促进核分割技术的进步。然而，由于数量不足、单一数据源和低分割难度，公共数据集不足以进行合理和全面的评估。
因此，我们提供了最大的用于宫颈核分割（CNSeg）的数据集。它包含了从 1530 名不同条件下的患者中收集的 124000 个标注核。该数据集的图像风格涵盖了大多数实际应用场景，包括微生物感染、细胞病变异质性、重叠核等。为了从不同方面评估分割方法的性能，我们将 CNSeg 数据集分为三个子集，即采集复杂条件下核图像的斑块分割数据集（PatchSeg）、包含聚类核的聚类分割数据集（ClusterSeg）和来自不同领域的数据域分割数据集（DomainSeg）。此外，我们提出了一种后处理方法，可将重叠核逐个处理。
实验表明，我们的数据集可以从不同方面全面评估宫颈核分割方法。我们为其他研究人员提供了使用数据集的指南。https://github.com/jingzhaohlj/AL-Net。
