Image data set from a multi-spectral animal imaging system is used to address two issues: (a) registering the oscillation in optical coherence tomography (OCT) images due to mouse eye movement and (b) suppressing the shadow region under the thick vessels/structures. Several classical and AI-based algorithms in combination are tested for each task to see their compatibility with data from the combined animal imaging system. Hybridization of AI with optical flow followed by Homography transformation is shown to be working (correlation value>0.7) for registration. Resnet50 backbone is shown to be working better than the famous U-net model for shadow region detection with a loss value of 0.9. A simple-to-implement analytical equation is shown to be working for brightness manipulation with a 1% increment in mean pixel values and a 77% decrease in the number of zeros. The proposed equation allows formulating a constraint optimization problem using a controlling factor {\alpha} for minimization of number of zeros, standard deviation of pixel value and maximizing the mean pixel value. For Layer segmentation, the standard U-net model is used. The AI-Pipeline consists of CNN, Optical flow, RCNN, pixel manipulation model, and U-net models in sequence. The thickness estimation process has a 6% error as compared to manual annotated standard data.

翻译：利用多光谱动物成像系统的图像数据集解决两个问题：（a）校正小鼠眼球运动引起的光学相干断层扫描（OCT）图像振荡；（b）抑制厚血管/结构下方的阴影区域。针对每项任务测试了多种经典算法与基于AI的算法组合，以评估其与混合动物成像系统数据的兼容性。研究表明，将AI与光流法及单应性变换相结合可实现图像配准（相关系数>0.7）。在阴影区域检测任务中，Resnet50骨干网络的效果优于著名的U-net模型，损失值达0.9。提出一种简易实现的解析方程用于亮度调控，可使像素均值提升1%，零像素数量减少77%。该方程通过控制因子{\alpha}构建约束优化问题，旨在最小化零像素数与像素标准差，同时最大化像素均值。分层分割采用标准U-net模型。该AI处理流程依次包含CNN、光流法、RCNN、像素调控模型及U-net模型。相比人工标注标准数据，厚度估计误差为6%。