Alabi Oluwatosin, Toe Ko Ko Zayar, Zhou Zijian, Budd Charlie, Raison Nicholas, Shi Miaojing, Vercauteren Tom
Kings College London, Surgical & Interventional Engineering, London, SE1 7EU, United Kingdom.
Kings College Hospital Denmark Hill, department, London, SE5 9RS, United Kingdom.
Sci Data. 2025 May 20;12(1):825. doi: 10.1038/s41597-025-05163-w.
In laparoscopic and robotic surgery, precise tool instance segmentation is an essential technology for advanced computer-assisted interventions. Although publicly available procedures of routine surgeries exist, they often lack comprehensive annotations for tool instance segmentation. Additionally, the majority of standard datasets for tool segmentation are derived from porcine(pig) surgeries. To address this gap, we introduce CholecInstanceSeg, the largest open-access tool instance segmentation dataset to date. Derived from the existing CholecT50 and Cholec80 datasets, CholecInstanceSeg provides novel annotations for laparoscopic cholecystectomy procedures in patients. Our dataset comprises 41.9k annotated frames extracted from 85 clinical procedures and 64.4k tool instances, each labelled with semantic masks and instance IDs. To ensure the reliability of our annotations, we perform extensive quality control, conduct label agreement statistics, and benchmark the segmentation results with various instance segmentation baselines. CholecInstanceSeg aims to advance the field by offering a comprehensive and high-quality open-access dataset for the development and evaluation of tool instance segmentation algorithms.
在腹腔镜手术和机器人手术中，精确的工具实例分割是先进的计算机辅助干预的一项关键技术。尽管存在常规手术的公开可用程序，但它们往往缺乏用于工具实例分割的全面注释。此外，大多数用于工具分割的标准数据集都来自猪的手术。为了弥补这一差距，我们引入了CholecInstanceSeg，这是迄今为止最大的开放获取工具实例分割数据集。CholecInstanceSeg源自现有的CholecT50和Cholec80数据集，为患者的腹腔镜胆囊切除术提供了新的注释。我们的数据集包括从85个临床手术中提取的41900个带注释的帧和64400个工具实例，每个实例都带有语义掩码和实例ID。为确保注释的可靠性，我们进行了广泛的质量控制，进行了标签一致性统计，并用各种实例分割基线对分割结果进行了基准测试。CholecInstanceSeg旨在通过提供一个全面、高质量的开放获取数据集来推动该领域的发展，以用于工具实例分割算法的开发和评估。
