Automated decision support for Hallux Valgus treatment options using anteroposterior foot radiographs

Figure 3 Data flow in the proposed approach. A: Bones are manually segmented and labelled from anonymized input radiographs to perform multi-class segmentation using a U-Net neural network; B: Radiographs are then randomly assigned to three subsets: training, validation, and testing; C: The accuracy of bone segmentation in each training cycle of the U-Net is validated on a fixed validation subset consisting of 20 radiographs. The U-Net network is trained on a training subset initially consisting of 50 radiographs, which is increased by 10 each training cycle until achieving average Sørensen–Dice index (SDI) > 97% on the validation set; D: Once the network achieves an SDI > 0.97, calculated on the testing subset, the U-Net model completes. If SDI is not > 0.97, the training subset is extended and the U-Net is retrained; E: The final U-Net model is used to segment and label bones on all testing radiographs; F: These are used to automatically determine reference points and measure hallux valgus and intermetatarsal angles.