Dr. Junya Sato Profile

Dr. Junya Sato

at Osaka Univ

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Proceedings Article | 13 March 2024 Poster + Paper

Deep UV-excited fluorescence microscopy with CycleGAN-assisted image translation for intraoperative detection of lymph node metastasis

Ryuta Nakao, Junya Sato, Ippei Takada, Hirohiko Niioka, Tetsuro Takamatsu

Proceedings Volume 12831, 128310J (2024) https://doi.org/10.1117/12.3002339

KEYWORDS: Deep convolutional neural networks, Cancer detection, Tissues, Cancer, Terbium, Lymph nodes, Image classification, Diagnostics, Deep ultraviolet, Fluorescence microscopy

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Lymph node (LN) metastasis is one of the most important prognostic factors in several common malignancies such as gastric cancer and breast cancer. The frozen section method is widely used for intraoperative pathological diagnosis. However, there are some issues with this process. In other words, experience is essential for specimen preparation and diagnosis, and freezing causes severe tissue damage. Microscopy with ultraviolet surface excitation (MUSE) has potential to provide rapid diagnosis with simple technique comparing to conventional histopathology based on hematoxylin and eosin (H&E) staining. We established a fluorescent staining protocol for Deep UV-excitation fluorescence imaging by using terbium ion and Hoechst 33342 that has enabled clear discrimination of nucleoplasm, nucleolus, and cytoplasm. In formalin-fixed paraffin-embedded (FFPE) thin-sliced tissue sections of metastasis-positive/- negative LNs of gastric cancer patients, the performance of cancer detection by patch-based training with a deep convolutional neural network (DCNN) on the fluorescence images was comparable with that of H&E images. However, MUSE images from non-thin-sliced tissue are difficult for pathologists to label training data for a supervised learning manner. We attempt a deep-learning pipeline model for LN metastasis detection, in which CycleGAN translates MUSE images to FFPE thin-sliced tissue images, and diagnostic prediction is performed using deep convolutional neural network trained on FFPE images. The modality translation using CycleGAN was able to improve the pathological diagnosis of non-thin-sliced surface images using DCNN model trained by FFPE images.

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