## 3 Results
### Model Details and Rationale for Overfitting  
As described earlier, the accuracy of the model on new data (i.e., the image stacks from the eight subjects used for validation) was 95%. To achieve these results, we evaluated the training accuracy and loss for both the large and small image recognition algorithms (Figure 7). Training accuracy and loss were computed by TensorFlow automatically during the training process, and so, only image stacks from the eight training participant were used to generate these plots. The training accuracy corresponded to the percent identified correctly within the subset of the volume stacks used to train the algorithm. The validation accuracy represents the percentage of images that the program correctly identified within the remaining volume stacks. Thus, training accuracy will always be higher than validation accuracy. The training loss represents the penalty involved in failing to identify an image correctly and goes up with overfitting the models. The \(x\)-axis on each graph was measured in samples. Each sample represents one set of training data, and we used 45 samples per trained model. Each sample contained 182 images, and so, the program plotted its training accuracy and loss every 150 images. This was because 20% of the total data was saved randomly to be tested through the model every time the set of 150 images was processed. This means that every time a set of training images (150) was used, the program tested the validation data of all 45 image sets, leading each image set to validate using the same 1440 images for all of them. Thus, the orange line representing validation accuracy and validation loss used 1440 of the same images every time and corresponded to more training data being input over time. For the large image recognition algorithm, the training loss of our models declined to near 0%, whereas the validation loss ended up at around 17% (Figure 7A). The output of this algorithm was then fed into the small image recognition algorithm. The training loss was 0% and the validation loss ended up at about 16% (Figure 7B). Both algorithms had accuracies of 100% on the training data and ~98% on the validation data. These data mean that the final output of the two-stage algorithm, as shown in Figure 7B, was quite good. However, the validation loss indicated overfitting. We kept this because, on a practical basis, it was not so overfitted as to adversely affect the functional output of the algorithm. In fact, we purposefully kept the algorithms slightly overfit, as we recognized that the additional training cycles led to improved TM detection by eye, even though this  
Figure 6: **Examples of artifacts that could and could not be removed from the TM. ** (**A**) One slice from an original volume stack. Artifacts are identified (_red asterisks_). (**B**) The TM object from the same slice. Note that while the cochlea and most of the artifact was segmented out, some residual artifact remained at the bottom of the TM. (**C**) The original 3D volume projection contained many artifacts. (**D**) The segmented 3D volume projection of the TM was much cleaner. However, there was still some artifact within the TM that could not be segmented out. was not represented in these plots. To demonstrate this, we re-created the small image recognition algorithm using only 25% of the data (Figure 7C). After completing all of the training, the accuracy and training loss were similar to what was found using the full training set (i.e., Figure 7B), but the validation loss was only \(\sim\)10%. We then visually inspected the images that were output using new data and found that the overfitted model had slightly better artifact removal (Figure 8). Figure 8: **A representative 3D image of the TM before and after segmentation. ****(A)** The original image. Note artifacts in the upper left. (**B**) The image after segmentation of the TM using the overfit algorithm from Figure 7B. (**C**) The image after segmentation of the TM using the algorithm from Figure 7C.