You Joshua Yedam, Eom Minho, Choi Tae-Ik, Cho Eun-Seo, Choi Jieun, Lee Minyoung, Shin Changyeop, Moon Jieun, Kim Eunji, Kim Pilhan, Kim Cheol-Hee, Yoon Young-Gyu
School of Electrical Engineering, KAIST, Daejeon, Republic of Korea.
Department of Biology, Chungnam National University, Daejeon, Republic of Korea.
Cell Rep Methods. 2025 Jun 16;5(6):101074. doi: 10.1016/j.crmeth.2025.101074. Epub 2025 Jun 9.
Analysis of biological samples often requires integrating diverse imaging modalities to gain a comprehensive understanding. While supervised biomedical image translation methods have shown success in synthesizing images across different modalities, they require paired data, which are often impractical to obtain due to challenges in data alignment and sample preparation. Unpaired methods, while not requiring paired data, struggle to preserve the precise spatial and quantitative information essential for accurate analysis. To address these challenges, we introduce STABLE (spatial and quantitative information preserving biomedical image translation), an unpaired image-to-image translation method that emphasizes the preservation of spatial and quantitative information by enforcing information consistency and employing dynamic, learnable upsampling operators to achieve pixel-level accuracy. We validate STABLE across various biomedical imaging tasks, including translating calcium imaging data from zebrafish brains and virtual histological staining, demonstrating its superior ability to preserve spatial details, signal intensities, and accurate alignment compared to existing methods.
对生物样本的分析通常需要整合多种成像模态，以获得全面的理解。虽然有监督的生物医学图像翻译方法在跨模态合成图像方面已取得成功，但它们需要配对数据，由于数据对齐和样本制备方面的挑战，这些数据往往难以获取。非配对方法虽然不需要配对数据，但难以保留准确分析所需的精确空间和定量信息。为应对这些挑战，我们引入了STABLE（保留空间和定量信息的生物医学图像翻译），这是一种非配对的图像到图像翻译方法，通过强制信息一致性并采用动态、可学习的上采样算子来实现像素级精度，从而强调保留空间和定量信息。我们在各种生物医学成像任务中验证了STABLE，包括对斑马鱼大脑的钙成像数据进行翻译和虚拟组织学染色，结果表明与现有方法相比，它在保留空间细节、信号强度和精确对齐方面具有卓越能力。
