Noul Co., Ltd., Yongin-si, Gyeonggi-do, 16942, Republic of Korea.
Department of Medical Device Management and Research, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, 115 Irwon-ro, Gangnam-gu, Seoul, 06355, Republic of Korea.
Sci Data. 2024 Oct 9;11(1):1106. doi: 10.1038/s41597-024-03938-1.
The White Blood Cell (WBC) differential test ranks as the second most frequently performed diagnostic assay. It requires manual confirmation of the peripheral blood smear by experts to identify signs of abnormalities. Automated digital microscopy has emerged as a solution to reduce this labor-intensive process and improve efficiency. Several publicly available datasets provide various WBC subtypes of differing quality and resolution. These datasets have contributed to advancing WBC classification using machine learning techniques. However, digital microscopy of blood cells with high magnification often requires a wider depth of field, posing challenges for automatic digital microscopy that necessitates capturing multiple stacks of focal planes to obtain complete images of specific blood cells. Our dataset provides 25,773 image stacks from 72 patients. The image labels consist of 18 classes encompassing normal and abnormal cells, with two experts reviewing each label. Each image includes 10 z-stacks of cropped 200 by 200 pixel images, captured using a 50X microscope with 400 nm intervals. This study presents a comprehensive multi-focus dataset for WBC classification.
白细胞（WBC）分类检测是仅次于最常进行的诊断检测项目。该检测需要专家手动确认外周血涂片，以识别异常迹象。自动化数字显微镜的出现解决了这个劳动密集型过程，并提高了效率。有几个公开的数据集提供了不同质量和分辨率的各种 WBC 亚型。这些数据集为使用机器学习技术进行 WBC 分类做出了贡献。然而，高倍放大的血细胞数字显微镜通常需要更大的景深，这对自动数字显微镜提出了挑战，因为自动数字显微镜需要捕获多个焦平面的堆栈，以获得特定血细胞的完整图像。我们的数据集提供了来自 72 名患者的 25,773 个图像堆栈。图像标签包含 18 个类别，涵盖正常和异常细胞，每个标签都由两位专家进行审查。每个图像包括 10 个裁剪为 200x200 像素的 z 堆叠图像，使用 50X 显微镜以 400nm 的间隔拍摄。本研究提出了一个用于 WBC 分类的综合多焦点数据集。
