Restoration of metabolic functional metrics from label-free, two-photon human tissue images using multiscale deep-learning-based denoising algorithms.
Restoration of metabolic functional metrics from label-free, two-photon human tissue images using multiscale deep-learning-based denoising algorithms.
复制标题
使用基于多尺度的基于深度学习的denoising算法，从无标签的两光子人体组织图像中恢复代谢功能指标。
DOI：
10.1117/1.jbo.28.12.126006
复制
发表时间：
2023-12
影响因子：
3.5
通讯作者：
Georgakoudi, Irene
中科院分区：
文献类型：
--
作者：
Vora, Nilay;Polleys, Christopher M.;Sakellariou, Filippos;Georgalis, Georgios;Thieu, Hong-Thao;Genega, Elizabeth M.;Jahanseir, Narges;Patra, Abani;Miller, Eric;Georgakoudi, Irene
关键词：
无标记、双光子激发荧光（TPEF）成像可捕获形态和功能代谢组织变化，从而增强对多种疾病的了解。然而，这些图像中存在的噪声和其他伪影使生物学有用信息的提取严重复杂化。我们的目标是在多尺度去噪算法的合成中采用深度神经架构，该算法经过优化，用于从低信噪比（SNR）TPEF图像中恢复代谢活动的指标。来自新鲜切除的人宫颈组织的还原烟酰胺腺嘌呤二核苷酸（磷酸）（NAD（P）H）和黄素蛋白（FAD）的TPEF图像用于评估各种去噪模型、预处理方法和数据对图像质量指标的影响，以及相对于地面实况图像，从图像恢复代谢功能的六个指标。使用基于小波变换域中的深度去噪的新算法来实现氧化还原比和线粒体组织的优化恢复。该算法还导致显着改善峰值信噪比（PSNR）和结构相似性指数测度（SSIM）的所有图像。有趣的是，其他模型产生甚至更高的PSNR和SSIM改善，但它们对于代谢功能指标的恢复不是最佳的。去噪算法可以从低SNR的无标记TPEF图像中恢复诊断上有用的信息，并将有助于这种成像的临床翻译。
Label-free, two-photon excited fluorescence (TPEF) imaging captures morphological and functional metabolic tissue changes and enables enhanced understanding of numerous diseases. However, noise and other artifacts present in these images severely complicate the extraction of biologically useful information. We aim to employ deep neural architectures in the synthesis of a multiscale denoising algorithm optimized for restoring metrics of metabolic activity from low-signal-to-noise ratio (SNR), TPEF images. TPEF images of reduced nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and flavoproteins (FAD) from freshly excised human cervical tissues are used to assess the impact of various denoising models, preprocessing methods, and data on metrics of image quality and the recovery of six metrics of metabolic function from the images relative to ground truth images. Optimized recovery of the redox ratio and mitochondrial organization is achieved using a novel algorithm based on deep denoising in the wavelet transform domain. This algorithm also leads to significant improvements in peak-SNR (PSNR) and structural similarity index measure (SSIM) for all images. Interestingly, other models yield even higher PSNR and SSIM improvements, but they are not optimal for recovery of metabolic function metrics. Denoising algorithms can recover diagnostically useful information from low SNR label-free TPEF images and will be useful for the clinical translation of such imaging.