公开1.2万张采血模拟图像数据集，支持医疗训练智能化
A labeled dataset of simulated phlebotomy procedures for medical AI: polygon annotations for object detection and human-object interaction
- 采用多边形标注五类医疗工具与人体部位
- 含70%训练、15%验证、15%测试集，兼容YOLOv8等框架
- 适用于医训系统、操作流程分析与自动化反馈
本数据文章发布了一个包含11,884张标注图像的数据集，记录了在训练手臂上进行的模拟采血（phlebotomy）过程。图像来自高清视频，在受控条件下录制，并通过结构相似性指数（SSIM）过滤减少冗余。所有视频在帧选择前均经过自动人脸匿名化处理。每张图像包含五类医学相关对象的多边形标注：注射器、橡皮筋、消毒棉片、手套和训练手臂。标注以现代目标检测框架（如YOLOv8）兼容的分割格式导出，确保广泛可用性。数据集按70%训练、15%验证、15%测试划分，旨在推动医疗培训自动化与人机交互研究。可应用于采血工具检测、操作步骤识别、工作流分析、合规性检查及提供结构化反馈的教育系统开发。数据与标注文件已公开发布于Zenodo。
原文摘要 · Abstract (English)
This data article presents a dataset of 11,884 labeled images documenting a simulated blood extraction (phlebotomy) procedure performed on a training arm. Images were extracted from high-definition videos recorded under controlled conditions and curated to reduce redundancy using Structural Similarity Index Measure (SSIM) filtering. An automated face-anonymization step was applied to all videos prior to frame selection. Each image contains polygon annotations for five medically relevant classes: syringe, rubber band, disinfectant wipe, gloves, and training arm. The annotations were exported in a segmentation format compatible with modern object detection frameworks (e.g., YOLOv8), ensuring broad usability. This dataset is partitioned into training (70%), validation (15%), and test (15%) subsets and is designed to advance research in medical training automation and human-object interaction. It enables multiple applications, including phlebotomy tool detection, procedural step recognition, workflow analysis, conformance checking, and the development of educational systems that provide structured feedback to medical trainees. The data and accompanying label files are publicly available on Zenodo.
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