Style transfer between Microscopy and Magnetic Resonance Imaging via Generative Adversarial Network in small sample size settings

• URL: http://arxiv.org/abs/2310.10414v1
• Date: Mon, 16 Oct 2023 13:58:53 GMT
• Title: Style transfer between Microscopy and Magnetic Resonance Imaging via Generative Adversarial Network in small sample size settings
• Authors: Monika Pytlarz, Adrian Onicas, Alessandro Crimi
• Abstract summary: Cross-modal augmentation of Magnetic Resonance Imaging (MRI) and microscopic imaging based on the same tissue samples is promising. We tested a method for generating microscopic histological images from MRI scans of the corpus callosum using conditional generative adversarial network (cGAN) architecture.
• Score: 49.84018914962972
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cross-modal augmentation of Magnetic Resonance Imaging (MRI) and microscopic imaging based on the same tissue samples is promising because it can allow histopathological analysis in the absence of an underlying invasive biopsy procedure. Here, we tested a method for generating microscopic histological images from MRI scans of the corpus callosum using conditional generative adversarial network (cGAN) architecture. To our knowledge, this is the first multimodal translation of the brain MRI to histological volumetric representation of the same sample. The technique was assessed by training paired image translation models taking sets of images from MRI scans and microscopy. The use of cGAN for this purpose is challenging because microscopy images are large in size and typically have low sample availability. The current work demonstrates that the framework reliably synthesizes histology images from MRI scans of corpus callosum, emphasizing the network's ability to train on high resolution histologies paired with relatively lower-resolution MRI scans. With the ultimate goal of avoiding biopsies, the proposed tool can be used for educational purposes.

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