Cholecystectomy is a very common abdominal surgical procedure almost ubiquitously performed with a laparoscopic approach, hence guided by an endoscopic video. Deep learning models for LC video analysis have been developed with the aim of assisting surgeons during interventions, improving staff awareness and readiness, and facilitating postoperative documentation and research. . However, datasets and models for video semantic segmentation of LC are lacking. Recognizing fine-grained hepatocystic anatomy through semantic segmentation could help surgeons better assess the critical view of safety (CVS), a universally recommended technique consisting in well exposing anatomical landmarks to prevent bile duct injuries. Additionally, segmentation masks of hepatocystic structures could be leveraged by deep learning models for automatic assessment of CVS and surgical action recognition to improve their performance. We believe that generating a dataset for video semantic segmentation of hepatocystic anatomy will promote surgical data science research and accelerate the development of applications for surgical safety. To generate a representative dataset, consecutive endoscopic videos of LC performed at Nouvel Hopital Civil (Strasbourg, France) were collected. Non-endoscopic, i.e., out-of-body, video frames were blackedout to comply with European data protection regulations. A frame every 30 seconds was sampled from the portion of the endoscopic video showing the hepatocystic anatomy being dissected, the most critical phase of the surgical procedure, and when surgeons should achieve the CVS. Such unselected and regularly spaced video frames were manually annotated with pixel-wise semantic annotations of anatomical and surgical instances, such as the cystic artery and the dissection. Overall, 1933 regularly spaced video frames from 201 LC videos were annotated with segmentation mask for 29 classes of the hepatocystic triangle, respectively. performed in double by specifically trained computer scientists and surgeons.