# Arthroscope Localization in 3D Ultrasound Volumes Using Weakly Supervised Deep Learning
## 4 Discussion
Therefore, in future work, it will be interesting to investigate the localization accuracy of the arthroscope independently from the classification provided by the algorithm. The ground truth labelling of the data used for network training was performed by the first author. However, a cross validation of the labels for the true positive cases was provided by three reviewers through visual inspection of the CAMs. Ideally, the performance of the algorithm should be evaluated using a segmentation of the arthroscope tip. However, obtaining a ground truth segmentation of the tip is challenging and time consuming and could possibly be affected by inter- and intra-operator variability. To obtain an objective measurement of the algorithm performance, a staple was attached to the arthroscope, which is easier to distinguish and segment. In the future, the coarse localization provided by the presented network may be used to make the segmentation of the arthroscope tip easier and less time consuming. First, a sub-volume can be extracted based on the CAM to reduce the time spent searching for the arthroscope. Secondly, the sub-volume can be sliced under an angle that agrees with the estimation of the arthroscope orientation to improve the visibility in a 2D view used for segmentation. It is promising that the algorithm has high classification accuracy (88.3%) and that localization is correct in 100% of the true positive cases in human cadaver data (see Figure 7). When inspecting the results of the cadaver volume more closely, 17 out of 18 false positives occurred in the first 19 2D images of the volume. On all these images a highly echogenic oblong structure created a strong response in the CAM, leading to incorrect classification. In the cadaver data, bone and tendons are visible highly echogenic on many slices of the volume, which limits the classification performance and makes the extraction of a sub-volume around the arthroscope difficult. It is important to note that none of the false positives were related to the presence of the hook-like surgical tool that was inserted into the cadaver knee as well. An aspect that has not been studied in detail in this work is the capability to use the algorithm in real-time and provide timely feedback to the surgeon during a knee arthroscopy. When using a standard laptop only \(\sim\)three images per second can be generated. Therefore, future work will be needed to reduce the required computational time. This can, for example, be conducted by using different hardware dedicated to this task, and the code can potentially be optimized to this end as well. A limitation of this study is that the network was trained and evaluated on only 71 volumes of phantom data and only one cadaver volume was used for further evaluation. However, this proof-of-concept shows that when training on phantom data, the model is capable of detecting the arthroscope in a cadaver US volume as well. This result shows that the network has the capability of transfer learning for detecting the arthroscope, which allows the network to be pre-trained on phantom data that can be easily obtained. Future work should focus on fine tuning the network on human data and should include an extensive evaluation on US data acquired during real knee arthroscopy scenarios. The limitations of this study can be addressed in future work in two ways. First, one should train the network on (a large amount of) human cadaver data to account for differences between the phantom data and data from an actual knee. Secondly, one should incorporate a 3D approach for detection that can utilize information of neighboring slices to distinguish an arthroscope from other highly echogenic structures. At the same time, a 3D approach can overcome the limitation of having to slice the volume in the plane where the arthroscope is visible as an oblong structure.