Two-stage error detection to improve electron microscopy image mosaicking

• Published in: Computers in biology and medicine 2024-08, Vol.178, p.108456, Article 108456
• Main Authors: Shi, Jiahao, Ge, Hongyu, Wang, Shuohong, Wei, Donglai, Yang, Jiancheng, Cheng, Ao, Schalek, Richard, Guo, Jun, Lichtman, Jeff, Wang, Lirong, Zhang, Ruobing
• Format: Article
• Language: English
• Subjects: Algorithms; Animals; Biomedical data; Brain - diagnostic imaging; Connectome - methods; Data acquisition; Datasets; Electron microscopy; Error correction & detection; Error detection; Humans; Image acquisition; Image processing; Image Processing, Computer-Assisted - methods; Image quality; Image reconstruction; Image stitching; Keypoint features; Microscopy; Microscopy, Electron - methods; Neuroimaging; Quality assessment; Quality control; Stitching; Stitching assessment; Tiles
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2024.108456

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. •Balancing electron microscopy mosaicking speed and accuracy is achievable.•Integrating varied features aids in precise, efficient EM image stitching.•EM stitched image quality assessment ensures precise stitching evaluation.