论文标题
Gloflow：从视频创建整个幻灯片图像的全球图像对齐
GloFlow: Global Image Alignment for Creation of Whole Slide Images for Pathology from Video
论文作者
论文摘要
深度学习对病理学的应用假设存在病理幻灯片的数字整体幻灯片图像的存在。但是，幻灯片数字化被幻灯片扫描仪的高度机动阶段的高成本瓶颈瓶颈，这是用于滑动缝制的位置信息所需的。我们提出了GloFlow，这是一种两阶段的方法，用于使用基于光流的图像编码来创建整个幻灯片图像，并使用可计算上的图形式图形方法进行全局对齐方式。在第一阶段，我们训练一个光流预测器，以预测连续的视频帧之间的成对翻译以近似针迹。在第二阶段，该近似针迹用于创建邻域图以产生校正的针迹。在WSIS视频扫描的模拟数据集上，我们发现我们的方法的表现优于幻灯片粘结的已知方法，并且针刺的WSIS类似于幻灯片扫描仪生产的方法。
The application of deep learning to pathology assumes the existence of digital whole slide images of pathology slides. However, slide digitization is bottlenecked by the high cost of precise motor stages in slide scanners that are needed for position information used for slide stitching. We propose GloFlow, a two-stage method for creating a whole slide image using optical flow-based image registration with global alignment using a computationally tractable graph-pruning approach. In the first stage, we train an optical flow predictor to predict pairwise translations between successive video frames to approximate a stitch. In the second stage, this approximate stitch is used to create a neighborhood graph to produce a corrected stitch. On a simulated dataset of video scans of WSIs, we find that our method outperforms known approaches to slide-stitching, and stitches WSIs resembling those produced by slide scanners.
