# Arthroscope Localization in 3D Ultrasound Volumes Using Weakly Supervised Deep Learning
## 2 Materials and Methods  
Eighty-one 3D US volumes were acquired using a VL13-5 linear array volumetric probe (Philips Healthcare, Bothell, WA, USA) and an EpiQ7 US system (Philips Medical Systems, Andover, MA, USA). The volumes were acquired on a phantom mimicking the sonographic appearance of human tissue layers with an arthroscope inserted. To this end, water, flour, minced meat, mozzarella cheese and tomatoes were combined in varying quantities inside a plastic container (see Figure 1). The materials used in this work are similar to materials that have been used for other phantoms, such as pea pods [37], grapes [38], tofu and paneer [39]. Minced meat has been used to mimic (human) tissue as it has a similar appearance, and it is easy to obtain compared to ex-vivo animal samples. Mozzarella and tomato [40] were added to create hyperechoic structures which can resemble other tissues like bone and muscle. Instead of using the more conventional gelatin, water with flour was used to ensure the appearance of US speckle patterns. The main advantage of using water with flour instead of gelatin is that this results in a dynamic phantom, which can change shape depending on how the arthroscope is inserted. This behavior is similar to a real a knee arthroscopy where the knee is filled with a saline solution and the tissues inside the knee can move under influence of the arthroscope. Furthermore, no traces, which could confuse the deep learning algorithm, are left in the phantom after removal of the arthroscope as is the case for gelatin. Other advantages of this phantom setup are that many different configurations of the structures can be created and, above all, that the complexity of the phantom can be increased from a simple single homogeneous component to a complex combination of materials with different echogenic characteristics. This was very helpful to guide the development of the algorithms in a progressive way. Table 1 details the different phantom types, material compositions, the number of US volumes and the corresponding number of 2D US images for each type. Phantom type 0 through 5 were used to create a training, validation and test set. (See below for more details). Phantom type 6 through 8 were used for performance evaluation of the network only (see below for more details). The acquired US volumes have varying image properties due to:  
Figure 1: Phantom setup. (**a**) Phantom composed of only water (Phantom0 type) with the arthroscope (**c**), US probe (**d**) and plastic container (**e**). (**b**) Phantom composed of water, flour and minced meat (Phantom5 type). In addition, one US volume that was acquired for a different study was used for testing. During this study, a knee of a human cadaver was injected with saline solution and an arthroscope was inserted into the knee from the lateral side. A hook-like surgical tool was inserted into the medial compartment of the knee to create a realistic knee arthroscopy scenario. This volume was not used to train the network in any way, only for evaluation. Four pre-processing steps were performed before network training. First, all voxels were resized by scaling all dimensions of each voxel to the largest voxel dimension (0.32 mm) present in the dataset. This is needed to preserve the aspect ratio of objects for different orientations because the network assumes the pixels are square. Secondly, the volumes were sliced into 2D images along the plane in which the arthroscope was visible as an oblong structure (in-plane). Thirdly, the 2D images were symmetrically padded with black (background) pixels to ensure that all the images had the same size as the largest 2D image in the dataset (119 \(\times\) 252 pixels). Finally, the size of the images was reduced by a factor of two (to 60 \(\times\) 126 pixels) to reduce the required computational cost for further processing. In order to provide the deep learning algorithm with weakly labeled data, each 2D image in the dataset was given an image-level label.