NuInsSeg：用于H&E染色组织学图像中细胞核实例分割的完整标注数据集
NuInsSeg: A Fully Annotated Dataset for Nuclei Instance Segmentation in H&E-Stained Histological Images
AI总结：
针对计算病理学中细胞核实例分割缺乏完整标注数据集的问题，本文发布了大规模手动标注数据集NuInsSeg，包含665个图像块、超30,000个细胞核及31种器官数据，并首次提供模糊区域掩码，为监督深度学习模型训练提供支持。
摘要：
在计算病理学中，自动细胞核实例分割在全切片图像分析中起着至关重要的作用。尽管已经提出了许多计算机化方法来完成这一任务，但与经典机器学习和图像处理技术相比，监督深度学习（DL）方法展现出了更优的分割性能。然而，这些模型需要完整标注的数据集进行训练，这在医学领域尤其具有挑战性。在本研究中，我们发布了Hematoxylin and Eosin (H&E)染色组织学图像中最大的完全手动标注细胞核数据集之一，称为NuInsSeg。该数据集包含665个图像块，涵盖来自31个人类和小鼠器官的超过30,000个手动分割的细胞核。此外，我们首次为整个数据集提供了额外的模糊区域掩码。这些模糊区域代表了图像中那些即使对人类专家而言也无法进行精确和确定性手动标注的部分。该数据集及生成相关分割掩码的详细分步说明分别公开于https://www.kaggle.com/datasets/ipateam/nuinsseg和https://github.com/masih4/NuInsSeg。
英文摘要：
In computational pathology, automatic nuclei instance segmentation plays an essential role in whole slide image analysis. While many computerized approaches have been proposed for this task, supervised deep learning (DL) methods have shown superior segmentation performances compared to classical machine learning and image processing techniques. However, these models need fully annotated datasets for training which is challenging to acquire, especially in the medical domain. In this work, we release one of the biggest fully manually annotated datasets of nuclei in Hematoxylin and Eosin (H&E)-stained histological images, called NuInsSeg. This dataset contains 665 image patches with more than 30,000 manually segmented nuclei from 31 human and mouse organs. Moreover, for the first time, we provide additional ambiguous area masks for the entire dataset. These vague areas represent the parts of the images where precise and deterministic manual annotations are impossible, even for human experts. The dataset and detailed step-by-step instructions to generate related segmentation masks are publicly available at https://www.kaggle.com/datasets/ipateam/nuinsseg and https://github.com/masih4/NuInsSeg, respectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
