Surgical tool detection

Cholec80 is an endoscopic video dataset containing 80 videos of cholecystectomy surgeries performed by 13 surgeons. The videos are captured at 25 fps and downsampled to 1 fps for processing. The whole dataset is labeled with the phase and tool presence annotations. The phases have been defined by a senior surgeon in Strasbourg hospital, France. Since the tools are sometimes hardly visible in the images and thus difficult to be recognized visually, a tool is defined as present in an image if at least half of the tool tip is visible. Source: EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos[https://arxiv.org/pdf/1602.03012.pdf]

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.

Analyzing the surgical workflow is a prerequisite for many applications in computer assisted surgery (CAS), such as context-aware visualization of navigation information, specifying the most probable tool required next by the surgeon or determining the remaining duration of surgery. Since laparoscopic surgeries are performed using an endoscopic camera, a video stream is always available during surgery, making it the obvious choice as input sensor data for workflow analysis. Moreover, this offers the opportunity for structured assessment of surgical skill for safety, teaching and quality management. The sub-challenge “Surgical Workflow and Skill Analysis” focuses on the online workflow analysis of laparoscopic surgeries. Participants are challenged to segment laparoscopic surgeries for gallbladder removal (cholecystectomy) into surgical phases, to recognize instrument presence and surgical actions as well as to classify surgical skill based on video data. Participants are encouraged (but not required!) to submit different results for phase segmentation, action recognition, instrument presence and skill classification . This novel kind of challenge investigates the current state-of-the-art results on surgical workflow analysis and skill assessment on one comprehensive dataset.

CholecTrack20 is a surgical video dataset focusing on laparoscopic cholecystectomy and designed for surgical tool tracking, featuring 20 annotated videos. The dataset includes detailed labels for multi-class multi-tool tracking, offering trajectories for tool visibility within the camera scope, intracorporeal movement within the patient's body, and the life-long intraoperative trajectory of each tool. Annotations cover spatial coordinates, tool class, operator identity, phase, visual conditions (occlusion, bleeding, smoke), and more for tools like grasper, bipolar, hook, scissors, clipper, irrigator, and specimen bag, with annotations provided at 1 frame per second across 35K frames and 65K instance tool labels. The dataset uses official splits, allocating 10 videos for training, 2 for validation, and 8 for testing. Chinedu Nwoye

Overview The Surgical Instruments Recognition Dataset is a groundbreaking collection of high-resolution images (1280x960 pixels) specifically designed for the recognition and categorization of surgical instruments. This dataset captures the intricate details and complexity of surgical tools, particularly when arranged in scenarios reminiscent of an operating room. ## Data Acquisition Our data collection approach was multifaceted to encompass a wide range of scenarios in which surgical instruments are typically viewed. We utilized: 1. Ceiling-mounted, program-controlled camera: This camera systematically rotated to capture the instruments from various angles. 2. Handheld camera: It was employed to take pictures of instruments in trays on the floor, creating images with natural shadows and lighting. 3. Real-world operating room imagery: We included close-up shots of instruments in use, offering a view akin to that of the surgical team, and capturing the tools in motion. ## Annotations The dataset adheres to the COCO results format, supporting object and keypoint detection tasks. For object detection, bounding boxes and class labels are provided for each instrument. Key point annotations identify precise instrument points, with over 10,000 instruments annotated throughout the dataset. ## Dataset Structure The dataset is divided into three parts: - Training set: 1788 images with annotations - Validation set: 200 images with annotations - Testing set: 185 images with annotations