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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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

Original languageEnglish
Title of host publicationMedical Imaging 2022
Subtitle of host publicationComputer-Aided Diagnosis
EditorsKaren Drukker, Khan M. Iftekharuddin
PublisherSPIE
ISBN (Electronic)9781510649415
DOIs
Publication statusPublished - 2022
EventMedical Imaging 2022: Computer-Aided Diagnosis - Virtual, Online
Duration: Mar 21 2022Mar 27 2022

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume12033
ISSN (Print)1605-7422

Conference

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

Keywords

  • Age progression
  • Bone X-ray
  • Generative adversarial networks
  • Image synthesis
  • Visual Turing test

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

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