Abstract：
Large-scale electron microscopy (EM) has enabled the reconstruction of brain connectomes at the synaptic level by serially scanning over massive areas of sample sections. The acquired big EM data sets raise the great challenge of image mosaicking at high accuracy. Currently, it simply follows the conventional algorithms designed for natural images, which are usually composed of only a few tiles, using a single type of keypoint feature that would sacrifice speed for stronger performance. Even so, in the process of stitching hundreds of thousands of tiles for large EM data, errors are still inevitable and diverse. Moreover, there has not yet been an appropriate metric to quantitatively evaluate the stitching of biomedical EM images. Here we propose a two-stage error detection method to improve the EM image mosaicking. It firstly uses point-based error detection in combination with a hybrid feature framework to expedite the stitching computation while maintaining high accuracy. Following is the second detection of unresolved errors with a newly designed metric of EM stitched image quality assessment (EMSIQA). The novel detection-based mosaicking pipeline is tested on large EM data sets and proven to be more effective and as accurate when compared with existing methods.
摘要：
大规模电子显微镜（EM）通过连续扫描大量样品切片，可以在突触水平上重建脑连接体。获取的大EM数据集提出了高精度图像镶嵌的巨大挑战。目前,它只是遵循为自然图像设计的传统算法，通常只由几块瓷砖组成，使用单一类型的关键点功能，这将牺牲速度以获得更强的性能。即便如此,在为大型EM数据拼接成千上万个瓷砖的过程中，错误仍然是不可避免的和多种多样的。此外，目前还没有一个合适的指标来定量评估生物医学EM图像的拼接.在这里，我们提出了一种两阶段的错误检测方法来改善EM图像的镶嵌。它首先使用基于点的错误检测与混合特征框架相结合，以加快拼接计算，同时保持高精度。以下是使用新设计的EM拼接图像质量评估（EMSIQA）度量对未解决错误的第二次检测。新颖的基于检测的马赛克管道在大型EM数据集上进行了测试，与现有方法相比，被证明更有效，更准确。
