Automatic segmentation of contrast-enhanced bolus in videofluorography images of swallowing using deep learning: a preliminary study

Objective: This study aimed to create a deep learning model for automated bolus segmentation on videofluorography (VFG) images of patients with healthy swallowing and dysphagia using the artificial intelligence deep learning segmentation method, and to assess the performance of the method. Study Design VFG images of 72 swallowing of 12 patients were continuously converted into 15 static images per second. In total, 3910 images were arbitrarily assigned to the training, validation, test 1, and test 2 datasets. In the training and validation datasets, images of colored bolus areas were prepared, along with original images. Using a U-Net neural network, a trained model was created after 500 epochs of training. The test datasets were applied to the trained model, and the performances of automatic segmentation (Jaccard index, Sørensen–Dice coefficient, and sensitivity) were calculated. Results: : All performance values for the segmentation of the test 1 and 2 datasets were high, exceeding 0.9. Conclusion: Using an artificial intelligence deep learning segmentation method, we automatically segmented the bolus areas on VFG images; our method exhibited high performance. This model also allowed assessment of aspiration and laryngeal invasion.