CholecT50 is a dataset of endoscopic videos of laparoscopic cholecystectomy surgery introduced to enable research on fine-grained action recognition in laparoscopic surgery. It is annotated with triplet information in the form of <instrument, verb, target>. The dataset is a collection of 50 videos consisting of 45 videos from the Cholec80 dataset and 5 videos from an in-house dataset of the same surgical procedure. CholecT50 is an extension of CholecT40 with 10 additional videos and standardized classes. CholecT45 is a subset of CholecT50 consisting of 45 videos from the Cholec80 dataset and first public release of CholecT50. The following are the official variants of the dataset: - 1. CholecT50 : the original version as used in the Rendezvous publication. - ** 2.CholecT50 (challenge)** : the variant used in CholecTriplet challenge. - ** 3. CholecT50 (cross-val): official cross validation split of CholecT50. - ** 4. CholecT45 (cross-val): official cross validation split of CholecT45. - ** 5. CholecT40** : the original version of CholecT40 as used in Tripnet publication. The complete dataset split information is given here.
CholecT45 is a subset of CholecT50 consisting of 45 videos from the Cholec80 dataset. It is the first public release of part of CholecT50 dataset. CholecT50 is a dataset of 50 endoscopic videos of laparoscopic cholecystectomy surgery introduced to enable research on fine-grained action recognition in laparoscopic surgery. It is annotated with 100 triplet classes in the form of <instrument, verb, target>. - See CholecT50 for more information. - The dataset split is given here.
PETRAW data set was composed of 150 sequences of peg transfer training sessions. The objective of the peg transfer session is to transfer 6 blocks from the left to the right and back. Each block must be extracted from a peg with one hand, transferred to the other hand, and inserted in a peg at the other side of the board. All cases were acquired by a non-medical expert on the LTSI Laboratory from the University of Rennes. The data set was divided into a training data set composed of 90 cases and a test data set composed of 60 cases. A case was composed of kinematic data, a video, semantic segmentation of each frame, and workflow annotation.
CholecT40 is the first endoscopic dataset introduced to enable research on fine-grained action recognition in laparoscopic surgery. It consists of 40 videos of laparoscopic cholecystectomy surgery annotated with triplet information in the form of <instrument, verb, target>. The annotations spans over 128 triplet classes that are composed from 6 classes of surgical instruments, 8 classes of action verbs, and 19 classes of surgical targets. The dataset is used as benchmark for developing deep learning solution for the recognition of surgical activities in the form of a triplet. It is first surgical data science effort to replicate activity recognition in the same level as human-object interaction (HOI) in natural vision tasks. The parent dataset is CholecT50.