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：请下载所有文件，将其放入单个文件夹后，使用7-Zip将拆分的文件重组为完整数据集。 合成手术室台（Synthetic Operating Room Table，简称SORT）数据集是一款聚焦于计算机视觉任务的大规模数据集，用于实现手术台面上手术器械图像的实例计数、分割与定位。该数据集内的图像均通过虚幻游戏引擎（Unreal Game Engine）渲染生成，并依托UnrealCV插件完成标注（Qui, 2017）。 SORT数据集包含1个容器类、1个耗材类（纱布，gauze）以及6类手术器械，分别为：止血钳、手术刀、镊子（pincettes，即tweezers）、注射器、牙周膜刀与手术剪。除容器类采用3种不同的3D模型外，其余各类别的每个实例均配有两种不同的3D模型，且两种模型的出现概率均等。 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）的比例划分，三个子集分别包含365469、121951与122142个独立的物体实例。 本数据集的研发目标是开发基于计算机视觉的手术器械与耗材计数方法，以协助医护人员避免手术器械遗留在患者体内，进而引发慢性疼痛、脓毒症等并发症。当前此类清点工作均依靠人工完成，世界卫生组织（World Health Organization，简称WHO）建议由两名医护人员共同完成人工计数（Biswas, 2012），通常会在手术前后对台面上摆放的器械进行清点。这种记录手术中使用的器械或耗材的类型与数量的标准操作，并非额外的管理负担，而是预防手术中器械、耗材或物料遗留在患者体内的关键举措——此类遗留问题会延长患者的康复时间，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. and Wang, Y., 2017, October. Unrealcv: Virtual worlds for computer vision. In Proceedings of the 25th ACM international conference on Multimedia (pp. 1221-1224) → 邱伟、钟峰、张毅、乔松、肖哲、Kim T.S.、王岩. 2017年10月. UnrealCV：面向计算机视觉的虚拟世界. 收录于第25届ACM国际多媒体大会论文集（第1221-1224页） 2. 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 → R. Biswas、S. Ganguly、M. Saha、S. Saha、S. Mukherjee及A. Ayaz. 《纱布瘤与外科医生——当前法医学视角综述》. 《印度外科杂志》, 74(4):318–322, 2012年