Surgical LAparoscopic Motions

Laparoscopic surgery has been widely used in various surgical fields due to its minimally invasive and rapid recovery benefits. However, it requires surgeons to have exceptional technical expertise to overcome challenges such as limited surgical space, restricted visual feedback, and the need for high precision. The rapid development of computer vision and deep learning in surgical action recognition provides essential technical support for improving surgical navigation, training, and postoperative evaluation. Despite this progress, existing publicly available datasets have limited scale, high homogeneity, and inconsistent labeling quality, making it challenging to meet the needs of deep learning models. To address the above issues, we developed the dataset (Surgical LAparoscopic Motions), which encompasses various surgical types. The dataset includes annotations for seven key actions: Abdominal Entry, Use Clip, Hook Cut, Suturing, Panoramic View, Local Panoramic View, and Suction. Experienced medical experts meticulously reviewed the annotations to ensure high quality and clinical accuracy. This dataset aims to promote the development of laparoscopic surgical action recognition and artificial intelligence-driven surgery, supporting intelligent surgical robots and surgical automation.

腹腔镜手术（Laparoscopic surgery）凭借其微创性与术后恢复快的优势，已在各类外科领域得到广泛应用。然而，该术式对术者的专业技术功底要求极高，需攻克手术空间有限、视觉反馈受限、操作精度要求严苛等诸多挑战。计算机视觉与深度学习在手术动作识别领域的快速发展，为优化手术导航、手术培训与术后评估提供了关键技术支撑。尽管取得了上述进展，当前公开可用的手术数据集普遍存在规模有限、同质性过高、标注质量参差不齐等问题，难以满足深度学习模型的训练需求。为解决上述问题，我们研发了手术腹腔镜运动数据集（Surgical LAparoscopic Motions），该数据集涵盖多种外科手术类型。数据集包含7类核心手术动作的标注：腹部穿刺进入、使用止血夹、电钩切割、缝合、全景视野操作、局部全景视野操作以及吸引操作。经验丰富的医学专家对标注结果进行了细致审核，以确保数据集的高质量与临床准确性。本数据集旨在推动腹腔镜手术动作识别与人工智能驱动手术的发展，为智能手术机器人与手术自动化技术提供支持。