BAPGAN: GAN-based bone age progression of femur and phalange x-ray images

Shinji Nakazawa; Changhee Han; Joe Hasei; Ryuichi Nakahara; Toshifumi Ozaki

doi:10.1117/12.2608065

4 April 2022 BAPGAN: GAN-based bone age progression of femur and phalange x-ray images

Shinji Nakazawa, Changhee Han, Joe Hasei, Ryuichi Nakahara, Toshifumi Ozaki

Proceedings Volume 12033, Medical Imaging 2022: Computer-Aided Diagnosis; 120331A (2022) https://doi.org/10.1117/12.2608065
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

Convolutional Neural Networks play a key role in bone age assessment for investigating endocrinology, genetic, and growth disorders under various modalities and body regions. However, no researcher has tackled bone age progression/regression despite its valuable potential applications: bone-related disease diagnosis, clinical knowledge acquisition, and museum education. Therefore, we propose Bone Age Progression Generative Adversarial Network (BAPGAN) to progress/regress both femur/phalange X-ray images while preserving identity and realism. We exhaustively confirm the BAPGAN’s clinical potential via Fr´echet Inception Distance, Visual Turing Test by two expert orthopedists, and t-Distributed Stochastic Neighbor Embedding.

Conference Presentation

Citation Download Citation

Shinji Nakazawa, Changhee Han, Joe Hasei, Ryuichi Nakahara, and Toshifumi Ozaki "BAPGAN: GAN-based bone age progression of femur and phalange x-ray images", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 120331A (4 April 2022); https://doi.org/10.1117/12.2608065

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