Video Dataset for Surgical Phase, Keypoint, and Instrument Recognition in Laparoscopic Surgery (PhaKIR)

Robotic- and computer-assisted minimally invasive surgery (RAMIS) is increasingly relying on computer vision methods for reliable instrument recognition and surgical workflow understanding. Developing such systems often requires large, well-annotated datasets, but existing resources often address isolated tasks, neglect temporal dependencies, or lack multi-center variability. We present the Surgical Procedure Phase, Keypoint, and Instrument Recognition (PhaKIR) dataset, comprising eight complete laparoscopic cholecystectomy videos recorded at three medical centers. The dataset provides frame-level annotations for three interconnected tasks: surgical phase recognition (485,875 frames), instrument keypoint estimation (19,435 frames), and instrument instance segmentation (19,435 frames). PhaKIR is, to our knowledge, the first multi-institutional dataset to jointly provide phase labels, instrument pose information, and pixel-accurate instrument segmentations, while also enabling the exploitation of temporal context since full surgical procedure sequences are available. It served as the basis for the PhaKIR Challenge as part of the Endoscopic Vision (EndoVis) Challenge at MICCAI 2024 to benchmark methods in surgical scene understanding, thereby further validating the dataset's quality and relevance. The dataset is publicly available upon request via the Zenodo platform.

Comparative validation of surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation in endoscopy: Results of the PhaKIR 2024 challenge

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.