# Arthroscope Localization in 3D Ultrasound Volumes Using Weakly Supervised Deep Learning
## 4 Discussion  
In this work, a weakly supervised deep-learning algorithm for the localization of an arthroscope in US volumes was presented that only requires an image-level label for each 2D image of a 3D US volume. The algorithm is capable of classifying the presence of an arthroscope in phantom US images with an accuracy of 88.6% and a standard deviation of 2.4%, which indicates it is able to generalize well. The capability of localizing the arthroscope is illustrated by the very high ratio of images in which the CAMs indicate the location correctly (100% for the fold with the highest classification accuracy). Even in challenging conditions, such as Figure 5a, where the brightness of the arthroscope is comparable to the surrounding pixels and other bright(er) structures are present in the volume as well, the algorithm is capable of localizing the arthroscope. Furthermore, the  
Figure 6: Examples of classification results with their corresponding CAMs from the worst performing fold. With correct (**a,b**) and wrong (**c,d**) localization by the CAM. Image (**c**) is deemed incorrect, because although the CAM response encompasses the arthroscope, the highest CAM response is not at the same location as the arthroscope. The arthroscope is indicated with white arrows. The scale applies to all sub-figures. Figure 7: Examples of classification results with their corresponding CAMs from the cadaver knee US volume. (**a**) True positive with correct localization by the CAM. (**b**) False positive. The arthroscope is indicated with white arrows. The scale applies to all sub-figures. distance between the bounding box drawn based on the class activation maps and the centroid of the staple (attached to the tip of the arthroscope) is 0.2 mm or smaller. This implies that the tip of the arthroscope is within the bounding box, and the localization performance of the network is sufficient to extract a sub-volume from the original US volume based on the CAMs. Even though the arthroscope may have moved slightly when removing the staple, the tip of the arthroscope would still be inside the bounding box if the arthroscope moved less than 2.3 mm. This follows from the fact that the centroid of the staple is \(\sim\)2.5 mm away from the arthroscope tip and from the results which show that the bounding box is at maximum 0.2 mm away from the centroid of the staple. Furthermore, the threshold used to binarize the CAMs could be tuned to always capture the arthroscope entirely. The extracted sub-volume could be input for a segmentation algorithm, or an algorithm to determine the position of the tip and orientation of the arthroscope. Thanks to the initial localization, such an algorithm would not be hampered by highly echogenic structures that lie outside the sub-volume. The output of such an algorithm could be used to register the 3D US with the 2D view of the arthroscope. The difference in classification accuracy between the best and worst fold can be largely explained by the difference in performance for two US volumes. These volumes have the two most complex phantom compositions (Phantom type 4 and 5) which lead to challenging conditions to classify the arthroscope and may indicate the need for more training data. Figures 4(a) and 5(a) show the results of the same image of US volume 42 for the best and worst fold which have a true positive and false negative classification, respectively. However, in both cases the CAM indicates the location of the arthroscope correctly. This shows that when the classification of the US image is not correct (the probability of an arthroscope being present is not higher than 0.5 according to the algorithm), the corresponding CAM can potentially still be used for arthroscope localization as the CAM values are significantly higher around the arthroscope than in other parts of the volume. This is also the case for the false negative example of Figure 4(c).