LAUSR

A urinary stone is a type of abnormality that occurs frequently in the urinary system. An automated segmentation of urinary stones is important for assisting medical doctors in early diagnosis and further treatment. While deep learning techniques are effective for image segmentation, they require a large number of datasets to achieve high accuracy. We proposed a GAN-based augmentation technique for creating synthetic images based on stone and non-stone mask inputs in order to improve the segmentation network’s performance by increasing the number and diversity of training data. The synthetic training images were generated from stone-contained images and stone-free images using existing stone ground truth and corresponding stone location maps, respectively. To segment urinary stones from full abdominal x-ray images, we trained the MultiResUnet model using both original stone-contained and our proposed synthetic samples. The proposed method obtained a 69.59% pixel-wise $F_{1}$ score and a 68.14% region-wise $F_{1}$ score, which achieved an improvement of 2.12% and 2.13%, respectively, over a model trained with only the original stone-contained dataset.