# Surgical Tool Detection in Open Surgery Videos
## 2 Related Work
### Surgical Dataset  
Because a large-scale dataset is essential for the improvement of the data-driven deep learning algorithms, a dataset for surgical tool detection in the open surgery domain is not available; however, several datasets in other surgical domains for various tasks have been released in recent years.  
Most datasets released so far are related to MIS or cataract surgery. The Cholec80 dataset [18] consists of endoscopic videos of 80 cholecystectomy procedures labeled with phase and tool presence annotations. There are seven categories of surgical tools, including grasper, hook, clipper, bipolar, irrigator, scissors, and specimen bag. Some of the videos in Cholec 80 are included in the M2 CAI16-tool dataset [19], which consists of 15 cholecystectomy videos with ground truth binary annotations of the tools. [PERSON] et al. [13] introduced a new dataset, M2 CAI16-tool-locations, which extends the M2 CAI16- tool dataset with the coordinates of spatial bounding boxes around surgical tools. In the same way, [PERSON] et al. [43] extended the Cholec80 dataset [18] with spatial annotations of surgical tool. ATLAS Dione [15] consists of 99 action video clips recorded in a simulated surgical scene of 10 surgeons performing six surgical tasks on the da Vinci Surgical System (dVSS) with surgical tool annotations. The JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS) [44; 45] was released as the first public dataset containing video and kinematic recordings of robotic surgical demonstrations, as well as gesture and skill annotations. [PERSON] et al. [20] introduced the ESAD dataset for surgeon action detection in real-world endoscopic videos. This dataset is annotated with a surgical tool bounding box and its action label. The CATARACTS dataset [17] contains 50 videos of cataract surgeries, which involves removing a clouded natural lens and replacing it with an artificial lens. Here, the presence of 21 surgical tools was manually annotated by two experts.  
Recently, some datasets for open surgery video analysis have been proposed to perform object detection. Surgery hands [42] was developed for hand detection in open surgery videos composed of publicly available videos of open surgery collected from YouTube and the annotation of spatial bounding boxes of the surgeon's hands. [PERSON] et al. [24] recorded seven different types of actual open surgery with an egocentric camera for detecting two similar surgical tools: scissors and needle holders. [PERSON] et al. [22] collected the videos taken from two different angles simulating injured open bowel repairing surgery for a tool and hand detection task. These videos contain four surgical tools: needle holder, forceps, scissors, and mosquito forceps. Another open surgery dataset is Annotated Videos of Open Surgery (AVOS), which contains 1997 videos scraped from YouTube and annotated with bounding boxes for surgical tools, including electrocautery, needle drivers, forceps, and hands, and 21 joint key points for hands and action. [PERSON] et al. [23] introduced a dataset recorded by the open surgery suturing simulation system and provided tool bounding box and tool and hand bounding box annotations including three surgical tools: needle drivers, forceps, and scissors. In these existing open surgery datasets, unlike ours, the videos are often collected from a simulator [22, 23], and if the videos are collected from actual open surgery, they only provide, at most, four types of surgical tool class annotations [24, 25].  
Behavioral analysis during surgery also has many potential applications for research, quality improvement, and education. The Multi View Operating Room (MVOR) dataset [46] is the first multi-view pose estimation dataset generated from real surgery recordings obtained in an operating room (OR) using three different views. The dataset has been manually annotated to provide both 2D and 3D upper-body poses.