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2025-10-11 更新
Biology-driven assessment of deep learning super-resolution imaging of the porosity network in dentin
Authors:Lauren Anderson, Lucas Chatelain, Nicolas Tremblay, Kathryn Grandfield, David Rousseau, Aurélien Gourrier
The mechanosensory system of teeth is currently believed to partly rely on Odontoblast cells stimulation by fluid flow through a porosity network extending through dentin. Visualizing the smallest sub-microscopic porosity vessels therefore requires the highest achievable resolution from confocal fluorescence microscopy, the current gold standard. This considerably limits the extent of the field of view to very small sample regions. To overcome this limitation, we tested different deep learning (DL) super-resolution (SR) models to allow faster experimental acquisitions of lower resolution images and restore optimal image quality by post-processing. Three supervised 2D SR models (RCAN, pix2pix, FSRCNN) and one unsupervised (CycleGAN) were applied to a unique set of experimentally paired high- and low-resolution confocal images acquired with different sampling schemes, resulting in a pixel size increase of x2, x4, x8. Model performance was quantified using a broad set of similarity and distribution-based image quality assessment (IQA) metrics, which yielded inconsistent results that mostly contradicted our visual perception. This raises the question of the relevance of such generic metrics to efficiently target the specific structure of dental porosity. To resolve this conflicting information, the generated SR images were segmented taking into account the specific scales and morphology of the porosity network and analysed by comparing connected components. Additionally, the capacity of the SR models to preserve 3D porosity connectivity throughout the confocal image stacks was evaluated using graph analysis. This biology-driven assessment allowed a far better mechanistic interpretation of SR performance, highlighting differences in model sensitivity to weak intensity features and the impact of non-linearity in image generation, which explains the failure of standard IQA metrics.
目前认为牙齿的机械感受系统部分依赖于通过延伸穿过牙本质的孔隙网络中的流体流动对成牙本质细胞的刺激。因此，可视化最小的亚微观孔隙血管需要共聚焦荧光显微镜所能达到的最高分辨率，这是当前的金标准。这极大地限制了视野范围，只能观察到非常小的样本区域。为了克服这一局限性，我们测试了不同的深度学习（DL）超分辨率（SR）模型，以允许更快地获取低分辨率图像，并通过后处理恢复最佳图像质量。对采用不同采样方案所采集的实验性配对的高分辨率和低分辨率共聚焦图像应用了三种有监督的二维SR模型（RCAN、pix2pix、FSRCNN）和一种无监督模型（CycleGAN），实现了像素尺寸的增加，分别为x2、x4、x8。模型的性能通过一系列基于相似性和分布的图像质量评估（IQA）指标进行了量化，得出了不一致的结果，这些结果大多与我们的视觉感知相矛盾。这引发了这样一个问题，即这些通用指标是否有效地针对牙齿孔隙的特定结构。为了解决这些冲突信息，我们对生成的SR图像进行了分割，同时考虑到孔隙网络的特定尺度和形态，并通过比较连通组件进行了分析。此外，还评估了SR模型在共聚焦图像堆栈中保留三维孔隙连通性的能力，采用了图形分析的方法。这种生物学驱动的评估可以更好地进行机制解释，突显了模型对微弱特征敏感性的差异以及图像生成中非线性的影响，这解释了标准IQA指标失败的原因。
论文及项目相关链接
摘要
牙齿机械感受系统部分依赖于牙本质内流体流动对成牙本质细胞的刺激。为观察微观结构，通常采用共聚焦荧光显微镜，但视野受限。本研究应用深度学习超分辨率技术处理图像，提升图像质量并扩大视野。对比了多种监督与非监督模型，以像素放大倍数评估性能。采用生物学驱动的评估方法分析模型性能，发现不同模型对弱特征敏感度不同，解释了一般评估指标不一致的原因。
关键见解
- 牙齿机械感受系统依赖于流体通过牙本质中的孔隙网络的流动来刺激Odontoblast细胞。
- 共聚焦荧光显微镜是观察牙齿微观结构的金标准，但视野受限。
- 深度学习超分辨率技术用于提高图像质量并扩大视野，通过处理低分辨率图像实现快速实验采集。
- 对比了多种监督与非监督深度学习模型，包括RCAN、pix2pix、FSRCNN和CycleGAN，以像素放大倍数评估性能。
- 模型性能评估采用图像质量评估指标，但结果不一致，与视觉感知相矛盾。
- 采用生物学驱动的评估方法分析模型性能，发现不同模型对弱特征敏感度不同，这解释了标准评估指标的不一致性。
- 超分辨率模型在保持3D孔隙连通性方面表现出差异，这对理解牙齿微观结构具有重要意义。
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