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Jia Liang

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Proceedings Article | 16 March 2020 Presentation + Paper

Bone suppression on chest radiographs with adversarial learning

Jia Liang, Yu-Xing Tang, You-Bao Tang, Jing Xiao, Ronald Summers

Proceedings Volume 11314, 1131409 (2020) https://doi.org/10.1117/12.2550868

KEYWORDS: Chest imaging, Bone, Radiography, Image quality standards, Data modeling, Chest, Tissues, Machine learning, Computer aided diagnosis and therapy, Convolutional neural networks

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Dual-energy (DE) chest radiography provides the capability of selectively imaging two clinically relevant materials, namely soft tissues, and osseous structures, to better characterize a wide variety of thoracic pathology and potentially improve diagnosis in posteroanterior (PA) chest radiographs. However, DE imaging requires specialized hardware and a higher radiation dose than conventional radiography, and motion artifacts some- times happen due to involuntary patient motion. In this work, we learn the mapping between conventional radiographs and bone suppressed radiographs. Specifically, we propose to utilize two variations of generative adversarial networks (GANs) for image-to-image translation between conventional and bone suppressed radio- graphs obtained by DE imaging technique. We compare the effectiveness of training with patient-wisely paired and unpaired radiographs. Experiments show both training strategies yield radio-realistic" radiographs with suppressed bony structures and few motion artifacts on a hold-out test set. While training with paired images yields slightly better performance than that of unpaired images when measuring with two objective image quality metrics, namely Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR), training with unpaired images demonstrates better generalization ability on unseen anteroposterior (AP) radiographs than paired training.

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