# Surgical Tool Detection in Open Surgery Videos
## 3 Dataset
### Dataset Collection and Annotation  
Because there is no dataset available that contains open surgery recordings via an egocentric camera, we record our own dataset. The actual plastic surgeries were recorded using Tobii cameras attached to the surgeon's head. The surgeries were recorded at Keio University Hospital. Video recording of the patients was approved by the Keio University School of Medicine Ethics Committee, and written informed consent was obtained from all patients or their parents. Our dataset contains 15 videos of seven different types of surgery, including serial resection of skin lesions, skin tumor resection, posterior pharyngeal flap, subcutaneous tumor resection, alveolar bone grafting, scar revision, and open reduction and internal fixation, which were performed by six surgeons. Images from each type of open surgery video are shown in Figure 2. Each surgery video is about 30 to 90 min long and was recorded at 25 frames per second (FPS). The videos are downsampled to 1 fps for processing to avoid frames where the scene changes very little. Each instance is annotated using a bounding box and its surgical tool category label. The frame size of each video is 1920 \(\times\) 1080 (pixels). We conducted the annotation using the coordinates of spatial bounding boxes around the tools using eight graduate students in the Department of Information and Computer Science; all annotations have been examined by an expert surgeon. We used the Virtual object Tagging Tool (VoTT) for annotation. VoTT is a Microsoft open-source tool for the annotation and labeling of image and video assets. A screenshot of its graphical user interface is shown in Figure 3.  
Figure 3: Screenshot captured while using VoTT for annotation. Figure 2: Examples of each type of open surgery in seven different types of surgery. All surgical tool categories that appeared in the recording video are annotated. Then, we eliminated the surgical tool categories that appear less than 10 times. As a result, our dataset contains 31 surgical tool categories for initial annotation. The list of categories is shown in Table 1, and examples of each surgical tool category are shown in Figure 4. We have focused on the annotation of the surgical tool in use and excluded the annotation of the surgical tool not in use (e.g., surgical tools placed on the operating table). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **Training Set** & **Validation Set** & **Test Set** & **Total** \\ \hline BiCLamp & 297 & 0 & 0 & 297 \\ Bipolar Forceps & 453 & 55 & 205 & 713 \\ Chisel & 42 & 0 & 11 & 53 \\ Cup & 65 & 18 & 11 & 94 \\ Drill & 75 & 0 & 0 & 75 \\ Electric Cautery & 1460 & 101 & 162 & 1723 \\ Forceps & 2885 & 155 & 4038 & 7078 \\ Gauze & 5480 & 469 & 1921 & 7870 \\ Hammer & 26 & 0 & 2 & 28 \\ Hook & 1114 & 191 & 167 & 1472 \\ Kidney Dish & 33 & 0 & 34 & 67 \\ Malleable Retractor & 121 & 0 & 0 & 121 \\ Mouth Gag & 4596 & 1208 & 1325 & 7129 \\ Nasogastric Tube & 1672 & 0 & 0 & 1672 \\ Needle Holders & 3449 & 531 & 1327 & 5307 \\ Pen & 21 & 0 & 0 & 21 \\ Petri Dish & 106 & 0 & 44 & 150 \\ Raspatory & 750 & 86 & 105 & 941 \\ Retractor & 3423 & 45 & 364 & 3832 \\ Ruler & 14 & 0 & 0 & 14 \\ Scalpel & 784 & 168 & 173 & 1125 \\ Scissors & 1869 & 422 & 620 & 2911 \\ Screwdriver & 79 & 0 & 0 & 79 \\ Skewer & 212 & 103 & 29 & 344 \\ Spoon & 27 & 0 & 0 & 27 \\ Suction Cannula & 3911 & 622 & 827 & 5360 \\ Suction Tube & 24 & 0 & 46 & 70 \\ Suture, Suture Needle & 3979 & 419 & 2023 & 6421 \\ Syringe & 347 & 96 & 144 & 587 \\ Trephine & 6 & 0 & 34 & 40 \\ Tweezers & 8114 & 1119 & 2833 & 12,066 \\ \hline Total Object Instances & 45,434 & 5808 & 16,445 & 67,687 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of surgical tool categories in our dataset, with the number of instances in each of the training, validation, and test sets. Surgical tool detection in open surgery exhibits several specific features.