The Effectiveness of Data Augmentation for Bone Suppression in Chest Radiograph using Convolutional Neural Network

Objective: Bone suppression of chest radiograph holds great promise to improve the localization accuracy in Image-Guided Radiation Therapy (IGRT). However, data scarcity has long been considered as the prime culprit of developing Convolutional Neural Networks (CNNs) models for the task of bone suppression. In this study, we explored the effectiveness of various data augmentation techniques for the task of bone suppression. Methods: In this study, chest radiograph and bone-free chest radiograph are derived from 59 high-resolution CT scans. Two CNN models (U-Net and Generative Adversarial Network (GAN)) were adapted to explore the effectiveness of various data augmentation techniques for bone signal suppression in the chest radiograph. Lung radiograph and bone-free radiograph were used as the input and target label, respectively. Impacts of six typical data augmentation techniques (flip, cropping, noise injection, rotation, shift and zoom) on model performance were investigated. A series of statistical evaluating metrics, including Peak Signal-To-Noise Ratio (PSNR), Structural Similarity (SSIM) and Mean Absolute Error (MAR), were deployed to comprehensively assess the prediction performance of the two networks under the six data augmentation strategies. Quantitative comparative results showed that different data augmentation techniques exhibited a varying degree of influence on the performance of CNN models in the task of CR bone signal suppression. Results: For the U-Net model, flips, rotation (10 to 20 degrees), all the shifts, and zoom (1/8) resulted in improved model prediction accuracy. By contrast, other studied augmentation techniques showed adverse impacts on the model performance. For the GAN model, it was found to be more sensitive to the studied augmentation techniques than the U-Net. Vertical flip was the only augmentation method that yielded enhanced model performance. Conclusion: In this study, we found that different data augmentation techniques resulted in a varying degree of impacts on the prediction performance of U-Net and GAN models in the task of bone suppression in CR. However, it remains challenging to determine the optimal parameter settings for each augmentation technique. In the future, a more comprehensive evaluation is still warranted to evaluate the effectiveness of different augmentation techniques in task-specific image synthesis.