Reliable recognition and localization of surgical instruments in endoscopic video recordings are foundational for a wide range of applications in computer- and robot-assisted minimally invasive surgery (RAMIS), including surgical training, skill assessment, and autonomous assistance. However, robust performance under real-world conditions remains a significant challenge. Incorporating surgical context - such as the current procedural phase - has emerged as a promising strategy to improve robustness and interpretability. To address these challenges, we organized the Surgical Procedure Phase, Keypoint, and Instrument Recognition (PhaKIR) sub-challenge as part of the Endoscopic Vision (EndoVis) challenge at MICCAI 2024. We introduced a novel, multi-center dataset comprising thirteen full-length laparoscopic cholecystectomy videos collected from three distinct medical institutions, with unified annotations for three interrelated tasks: surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation. Unlike existing datasets, ours enables joint investigation of instrument localization and procedural context within the same data while supporting the integration of temporal information across entire procedures. We report results and findings in accordance with the BIAS guidelines for biomedical image analysis challenges. The PhaKIR sub-challenge advances the field by providing a unique benchmark for developing temporally aware, context-driven methods in RAMIS and offers a high-quality resource to support future research in surgical scene understanding.

翻译：在内窥镜视频记录中可靠地识别与定位手术器械，是计算机辅助与机器人辅助微创手术（RAMIS）中广泛应用（包括手术培训、技能评估与自主辅助）的基础。然而，在真实世界条件下实现鲁棒性能仍是一项重大挑战。结合手术上下文（如当前程序阶段）已成为提高鲁棒性与可解释性的一种有前景的策略。为应对这些挑战，我们在MICCAI 2024的内窥镜视觉（EndoVis）挑战赛中组织了手术程序阶段、关键点与器械识别（PhaKIR）子挑战。我们引入了一个新颖的多中心数据集，包含从三家不同医疗机构收集的十三段完整腹腔镜胆囊切除术视频，并针对三个相互关联的任务提供了统一标注：手术阶段识别、器械关键点估计与器械实例分割。与现有数据集不同，我们的数据集支持在同一数据中联合研究器械定位与程序上下文，同时支持整合整个手术过程中的时序信息。我们依据生物医学图像分析挑战的BIAS指南报告了结果与发现。PhaKIR子挑战通过为RAMIS中开发具有时序感知、上下文驱动的方法提供独特基准，推动了该领域的发展，并为支持未来手术场景理解研究提供了高质量资源。