Deep Learning-Based Bone Segmentation of the Metatarsophalangeal Joint : Using an Automatic and an Interactive Approach

Abstract: The first Metatarsophalangeal (MTP) joint is essential for foot biomechanics and weight-bearing activities. Osteoarthritis in this joint can lead to pain, discomfort, and limited mobility. In order to treat this, Episurf Medical is working to produce individualized implants based on 3D segmentations of the joint. As manual segmentations are both time- and cost-consuming, and susceptible to human errors, automatic approaches are preferred. This thesis uses U-Net and DeepEdit as deep-learning based methods for segmentation of the MTP joint, with the latter being evaluated with and without user interactions. The dataset used in this study consisted of 38 CT images, where each model was trained on 30 images, and the remaining images were used as a test set. The final models were evaluated and compared with regards to the Dice Similarity Coefficient (DSC), precision, and recall. The U-Net model achieved DSC 0.944, precision 0.961, and recall 0.929. The automatic DeepEdit approach obtained DSC of 0.861, precision of 0.842, and recall of 0.891, while the interactive DeepEdit approach resulted in DSC of 0.918, precision of 0.912, and recall of 0.928. All pairwise comparisons in terms of precision and DSC showed significant differences (p<0.05), where U-Net had the highest performance, while the difference in recall was not found to be significant (p>0.05) for any comparison. The lower performances of DeepEdit compared to U-Net could be due to lower spatial resolution in the segmentations. Nevertheless, DeepEdit remains a promising method, and further investigations of unexplored areas could be addressed as future work.