Carmo Diedre S, Pezzulo Alejandro A, Villacreses Raul A, Eisenbeisz McKenna L, Anderson Rachel L, Dorin Sarah E Van, Rittner Letícia, Lotufo Roberto A, Gerard Sarah E, Reinhardt Joseph M, Comellas Alejandro P
School of Electrical and Computer Engineering, Universidade Estadual de Campinas, Campinas, 13083-852, Brazil.
Department of Internal Medicine, University of Iowa, Iowa City, 52242, USA.
Sci Data. 2025 Mar 7;12(1):402. doi: 10.1038/s41597-025-04709-2.
The field of supervised automated medical imaging segmentation suffers from relatively small datasets with ground truth labels. This is especially true for challenging segmentation problems that target structures with low contrast and ambiguous boundaries, such as ground glass opacities and consolidation in chest computed tomography images. In this work, we make available the first public dataset of ground glass opacity and consolidation in the lungs of Long COVID patients. The Long COVID Iowa-UNICAMP dataset (LongCIU) was built by three independent expert annotators, blindly segmenting the same 90 selected axial slices manually, without using any automated initialization. The public dataset includes the final consensus segmentation in addition to the individual segmentation from each annotator (360 slices total). This dataset is a valuable resource for training and validating new automated segmentation methods and for studying interrater uncertainty in the segmentation of lung opacities in computed tomography.
有监督的自动医学影像分割领域受限于带有真实标签的相对较小的数据集。对于具有挑战性的分割问题来说尤其如此，这些问题针对的是对比度低且边界模糊的结构，比如胸部计算机断层扫描图像中的磨玻璃影和实变。在这项工作中，我们提供了首个关于新冠长期症状患者肺部磨玻璃影和实变的公共数据集。“爱荷华 - 坎皮纳斯新冠长期症状数据集”（LongCIU）由三位独立的专家注释员构建，他们在不使用任何自动初始化的情况下，盲目地手动分割相同的90个选定轴向切片。该公共数据集除了包含每位注释员的单独分割结果外，还包括最终的一致分割结果（总共360个切片）。这个数据集对于训练和验证新的自动分割方法以及研究计算机断层扫描中肺部混浊分割的评分者间不确定性而言，是一个宝贵的资源。
