H&E Style Translation Using CycleGAN for Deep Ultraviolet-Excitation Fluorescence Images of Pancreatic Endoscopic Ultrasound-Fine Needle Aspiration/Biopsy Toward Slide-Free Rapid Pathology

DOI Web Site 参考文献21件
  • Koyama Yuki
    Department of Pathology and Cell Regulation, Graduate School of Medical Science, Kyoto Prefectural University of Medicine Department of Molecular Gastroenterology and Hepatology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine
  • Nakao Ryuta
    Department of Pathology and Cell Regulation, Graduate School of Medical Science, Kyoto Prefectural University of Medicine
  • Sato Junya
    Department of Radiology, Osaka University Graduate School of Medicine Department of Artificial Intelligence Diagnostic Radiology, Osaka University Graduate School of Medicine
  • Honda Mizuki
    Department of Pathology and Cell Regulation, Graduate School of Medical Science, Kyoto Prefectural University of Medicine Department of Surgical Pathology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine
  • Inamori Osamu
    Department of Surgical Pathology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine
  • Tanaka Noriyuki
    Department of Surgical Pathology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine
  • Morinaga Yukiko
    Department of Surgical Pathology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine
  • Konishi Eiichi
    Department of Surgical Pathology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine Department of Pathology, Kyoto Saiseikai Hospital
  • Harada Yoshinori
    Department of Pathology and Cell Regulation, Graduate School of Medical Science, Kyoto Prefectural University of Medicine
  • Tanaka Hideo
    Department of Pathology and Cell Regulation, Graduate School of Medical Science, Kyoto Prefectural University of Medicine
  • Yasuda Hiroaki
    Department of Molecular Gastroenterology and Hepatology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine
  • Itoh Yoshito
    Department of Molecular Gastroenterology and Hepatology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine
  • Nagahara Hajime
    D3 Center, Osaka University
  • Niioka Hirohiko
    Department of Data-Driven Innovation Initiative, Kyushu University
  • Takamatsu Tetsuro
    Department of Pathology and Cell Regulation, Graduate School of Medical Science, Kyoto Prefectural University of Medicine Department of Medical Photonics, Kyoto Prefectural University of Medicine

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<p>Endoscopic ultrasound-guided fine-needle aspiration/biopsy (EUS-FNA/B) is critical for determining treatment strategies for patients with pancreatic cancer. However, conventional pathological examination using hematoxylin and eosin (H&E) staining is time-consuming. Microscopy with ultraviolet surface excitation (MUSE) enables rapid pathological diagnosis without requiring slide preparation. This study explores the potential of combining MUSE imaging with a cycle-consistent generative adversarial network (CycleGAN), an image generation algorithm capable of learning translations without paired images, to enhance diagnostic workflows for pancreatic EUS-FNA/B. Thirty-five pancreatic specimens were stained with Terbium/Hoechst 33342, and deep ultraviolet (DUV) fluorescence images were captured by exciting the tissue surface. These fluorescence images, along with H&E-stained formalin-fixed, paraffin-embedded (FFPE) sections from the same specimens, were divided into 256 × 256-pixel segments for CycleGAN training. The algorithm was employed to translate pseudo-H&E images from MUSE test images. The pseudo-H&E images generated by the CycleGAN showed improved inter-pathologist agreement among three pathologists compared with the original MUSE images. We established a technique to perform MUSE imaging on small pancreatic samples obtained through EUS-FNA/B and confirmed that H&E-style translation using CycleGAN simplified interpretation for pathologists. Integrating MUSE imaging with CycleGAN has the potential to offer a rapid, cost-effective, and accurate diagnostic tool.</p>

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