# Surgical Tool Detection in Open Surgery Videos
## 6 Conclusions  
Surgical tool detection is an indispensable task to be tackled for the surgical scene. However, mainly because of the lack of a large-scale and diverse dataset, there is only a small body of research work on open surgery videos. We presented our large-scale egocentric dataset for open surgery and the extensive evaluation of conventional object detection methods. We collected 15 open surgery videos of 7 different types of surgery using a camera attached to the surgeon's head. The dataset consists of 67,687 annotated images with a bounding box around the surgical tool and its category label. Our dataset is superior in size and number of tool types compared with the existing open surgery dataset, which is often collected using a simulator. The statistics presented for the dataset illustrate that the dataset is imbalanced because many surgical tools are used for various purposes.  
Despite the baseline experiments showing promising results for the detection of surgery tools in open surgery, the AP is not still acceptable for critical medical applications. One promising direction in future work is finding strategies to overcome the difficulty in the detection of heavily imbalanced data. Planned future work will collect surgery videos of which surgery type is less frequent in our dataset considering the revealed fact that the occurrence of some surgery tools depends on the surgery type.  
Conceptualization, R.F.; data curation, R.F. and H.K.; formal analysis, R.F.; funding acquisition, H.K. and H.S.; investigation, R.F.; methodology, R.F.; project administration, H.K. and H.S.; resources, H.S.; supervision, H.S.; validation, R.F.; visualization, R.F.; writing--original draft, R.F.; writing--review and editing, R.H. and H.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by MHLW Health, Labour, and Welfare Sciences Research Grants Research on Medical ICT and Artificial Intelligence Program Grant Number 20 AC1004, the MIC/SCOPE #201603003, and JSPS KAKENHI Grant Number 22H03617.  
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of Keio University School of Medicine.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multicolumn{2}{c}{**Faster-RCNN**} & \multicolumn{2}{c}{**RetinaNet**} \\ \cline{2-6}  & **ResNet-50** & **ResNet-101** & **ResNet-X101** & **ResNet-50** & **ResNet-101** \\ \hline BiGlamp & - & - & - & - & - \\ Bipolar Forcps & 46.4 & 48.7 & 50.2 & 39.8 & 47.6 \\ Chisel & 0.19 & 0.0 & 0.5 & 0.3 & 0.1 \\ Cup & 18.5 & 14.7 & 11.0 & 22.0 & 19.1 \\ Drill & - & - & - & - & - \\ Electric Cautery & 49.7 & 46.7 & 58.4 & 49.4 & 45.4 \\ Foreps & 6.8 & 8.9 & 8.9 & 7.1 & 7.6 \\ Gauze & 17.3 & 18.5 & 15.7 & 19.1 & 16.7 \\ Hammer & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\ Hook & 28.5 & 44.9 & 46.2 & 29.4 & 29.5 \\ Kidney Dish & 10.4 & 12.3 & 14.2 & 17.3 & 3.7 \\ Malleable Retractor & - & - & - & - & - \\ Mouth Gag & 73.3 & 73.1 & 73.7 & 73.9 & 74.0 \\ Nasogastric Tube & - & - & - & - & - \\ Needle Holders & 30.6 & 29.1 & 30.3 & 29.2 & 31.1 \\ Pen & - & - & - & - & - \\ Petri Dish & 0.5 & 0.3 & 0.0 & 3.7 & 2.8 \\ Raspatory & 56.1 & 56.0 & 60.9 & 54.5 & 56.4 \\ Retractor & 12.1 & 12.6 & 11.3 & 8.7 & 10.2 \\ Ruler & - & - & - & - & - \\ Scalpel & 61.0 & 59.4 & 64.0 & 55.2 & 59.9 \\ Scissors & 19.4 & 21.4 & 24.6 & 19.5 & 21.6 \\ Screwdriver & - & - & - & - & - \\ Skewer & 63.5 & 71.6 & 77.3 & 60.6 & 66.2 \\ Spoon & - & - & - & - & - \\ Suction cannula & 46.1 & 48.9 & 46.9 & 42.3 & 42.8 \\ Suction Tube & 0.6 & 1.1 & 0.9 & 1.2 & 0.5 \\ Suture, Stutter Needle & 0.9 & 0.5 & 0.3 & 0.7 & 0.7 \\ Syringe & 25.0 & 21.9 & 27.9 & 14.5 & 18.0 \\ Trephine & 0.0 & 0.0 & 0.0 & 1.1 & 0.5 \\ Tweezers & 57.9 & 58.3 & 58.1 & 55.9 & 57.2 \\ \hline Average & 27.2 & 28.6 & 29.7 & 26.7 & 27.8 \\ \hline \hline \end{tabular}
\end{table}
Table 5: AP of baseline models per category for test set.