Cross-modality image synthesis from unpaired data using CycleGAN effects of gradient consistency loss and training data size

Yuta Hiasa, Yoshito Otake, Masaki Takao, Takumi Matsuoka, Kazuma Takashima, Aaron Carass, Jerry L. Prince, Nobuhiko Sugano, Yoshinobu Sato

Research output: Contribution to journalArticlepeer-review

Abstract

CT is commonly used in orthopedic procedures. MRI is used along with CT to identify muscle structures and diagnose osteonecrosis due to its superior soft tissue contrast. However, MRI has poor contrast for bone structures. Clearly, it would be helpful if a corresponding CT were available, as bone boundaries are more clearly seen and CT has a standardized (i.e., Hounsfield) unit. Therefore, we aim at MR-to-CT synthesis. While the CycleGAN was successfully applied to unpaired CT and MR images of the head, these images do not have as much variation of intensity pairs as do images in the pelvic region due to the presence of joints and muscles. In this paper, we extended the CycleGAN approach by adding the gradient consistency loss to improve the accuracy at the boundaries. We conducted two experiments. To evaluate image synthesis, we investigated dependency of image synthesis accuracy on 1) the number of training data and 2) incorporation of the gradient consistency loss. To demonstrate the applicability of our method, we also investigated segmentation accuracy on synthesized images.

Original languageEnglish (US)
JournalUnknown Journal
StatePublished - Mar 18 2018

Keywords

  • CT
  • CycleGAN
  • Image synthesis
  • MR
  • Musculoskeletal image
  • Segmentation

ASJC Scopus subject areas

  • General

Fingerprint Dive into the research topics of 'Cross-modality image synthesis from unpaired data using CycleGAN effects of gradient consistency loss and training data size'. Together they form a unique fingerprint.

Cite this