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

Shinji Nakazawa, Changhee Han, Jou Hasei, Ryuichi Nakahara, Toshihumi Ozaki

研究成果

抄録

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 Fŕechet Inception Distance, Visual Turing Test by two expert orthopedists, and t-Distributed Stochastic Neighbor Embedding.

本文言語English
ホスト出版物のタイトルMedical Imaging 2022
ホスト出版物のサブタイトルComputer-Aided Diagnosis
編集者Karen Drukker, Khan M. Iftekharuddin
出版社SPIE
ISBN(電子版)9781510649415
DOI
出版ステータスPublished - 2022
イベントMedical Imaging 2022: Computer-Aided Diagnosis - Virtual, Online
継続期間: 3月 21 20223月 27 2022

出版物シリーズ

名前Progress in Biomedical Optics and Imaging - Proceedings of SPIE
12033
ISSN(印刷版)1605-7422

Conference

ConferenceMedical Imaging 2022: Computer-Aided Diagnosis
CityVirtual, Online
Period3/21/223/27/22

ASJC Scopus subject areas

  • 電子材料、光学材料、および磁性材料
  • 原子分子物理学および光学
  • 生体材料
  • 放射線学、核医学およびイメージング

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