Synthetic Operating Room Table (SORT) Dataset

*Note: Please download all files, place them into a single folder and then use 7-Zip to recombine split files back into the complete dataset. The Synthetic Operating Room Table (SORT) dataset is a large-scale computer vision focused on instance counting, segmentation and localisation of surgical instrument depictions placed on a table. The depictions contained are rendered using the Unreal game engine and annotated leveraging the UnrealCV plugin (Qui, 2017). SORT contains one container class, one material class (gauze) and six instrument classes namely, forceps, scalpels, pincettes (tweezers), syringes, periotomes, and scissors. Each class contains two different 3D representations equally likely to be present for a given instance, with the exception of the container class that leverages three different 3D models. In total, we generated 89,838 images, split into 60% training (53,906), 20% validation (17,965), and 20% test (17,967), containing 365,469, 121,951 and 122,142 separate object instances, respectively. The aim behind this dataset is to develop methods to be able to count surgical instruments and materials via computer vision to aid medical staff in ensuring no instrument is retained by a patient, leading to complications such as chronic pain and sepsis. Currently, this is done manually, with the World Health Organisation (WHO) proposing that manual counts should be completed by two members of staff (Biswas, 2012), typically counting instruments laid out on a surface, either before or after their use. This standard practice of logging the type and number of a given instrument or material to be used during an operation is not managerial overhead but crucial for the prevention of retained instruments, consumables, or materials during surgery, as these would negatively impact a patient's recovery time or even lead to the patient's death. Qiu, W., Zhong, F., Zhang, Y., Qiao, S., Xiao, Z., Kim, T.S. and Wang, Y., 2017, October. Unrealcv: Virtual worlds for computer vision. In Proceedings of the 25th ACM international conference on Multimedia (pp. 1221-1224) R. Biswas, S. Ganguly, M. Saha, S. Saha, S. Mukherjee, and A. Ayaz. Gossypiboma and Surgeon - Current Medicolegal Aspect – A Review. Indian Journal of Surgery, 74(4):318–322, 2012

*注意：请下载全部文件并放入单个文件夹，随后使用7-Zip将分卷压缩包合并为完整数据集。
合成手术台（Synthetic Operating Room Table, SORT）数据集是一款面向计算机视觉任务的大规模数据集，聚焦手术器械在台面上的实例计数、分割与定位任务。该数据集的图像由虚幻引擎（Unreal Engine）渲染生成，并借助UnrealCV插件（Qui等，2017）完成标注。
SORT包含1个容器类、1个敷料类（纱布，gauze）以及6类手术器械，分别为：止血钳（forceps）、手术刀（scalpels）、镊子（pincettes，即tweezers）、注射器（syringes）、骨膜剥离子（periotomes）以及剪刀（scissors）。除容器类采用3种不同3D模型外，其余每类均包含两种出现概率均等的3D表示形式。
本次共生成89838张图像，按60%训练集（53906张）、20%验证集（17965张）、20%测试集（17967张）划分，对应分别包含365469、121951和122142个独立目标实例。
本数据集旨在开发基于计算机视觉的手术器械与耗材计数方法，以辅助医护人员避免手术器械遗留在患者体内，进而预防慢性疼痛、脓毒症等并发症。
目前此类计数工作均由人工完成，世界卫生组织（World Health Organization, WHO）建议由两名医护人员共同完成人工计数（Biswas等，2012），通常会对术前或术后放置在台面上的手术器械进行清点。这种记录手术中使用的器械及耗材类型与数量的标准操作虽会带来一定管理成本，但对预防手术中遗留器械、耗材或其他物品至关重要——此类遗留问题会延长患者康复周期，甚至导致患者死亡。
参考文献：
1. Qiu, W., Zhong, F., Zhang, Y., Qiao, S., Xiao, Z., Kim, T.S. 及 Wang, Y.，2017年10月。《UnrealCV：面向计算机视觉的虚拟世界》，收录于第25届ACM国际多媒体大会会议论文集（第1221-1224页）
2. R. Biswas、S. Ganguly、M. Saha、S. Saha、S. Mukherjee 及 A. Ayaz.《Gossypiboma与外科医生——当前法医学视角综述》，《印度外科杂志》，74(4):318–322，2012年