Svoboda | Graniru | BBC Russia | Golosameriki | Facebook
Skip to main content

Supporting data for "vEMstitch: an algorithm for fully automatic image stitching of volume electron microscopy"

Dataset type: Imaging, Software
Data released on September 11, 2024

He B; Zhang Y; Zhang Z; Cheng Y; Zhang F; Sun F; Han R (2024): Supporting data for "vEMstitch: an algorithm for fully automatic image stitching of volume electron microscopy" GigaScience Database. https://doi.org/10.5524/102574

DOI10.5524/102574

As software and hardware have developed, so has the scale of research into volume electron microscopy (vEM), leading to ever-increasing resolution. Usually, data collection is followed by image stitching: the same area is subjected to high-resolution imaging with a certain overlap, and then the images are stitched together to achieve ultrastructure with large scale and high resolution simultaneously. However, there is currently no perfect method for image stitching, especially when the global feature distribution of the sample is uneven and the feature points of the overlap area cannot be matched accurately, which results in the ghosting of the fusion area.
We have developed a novel algorithm called vEMstitch to solve these problems, aiming for seamless and clear stitching of high-resolution images. In vEMstitch, the image transformation model is constructed as a combination of global rigid and local elastic transformation using weighted pixel displacement fields. Specific local geometric constraints and feature re-extraction strategies are incorporated to ensure that the transformation model accurately and completely reflects the characteristics of biological distortions. To demonstrate the applicability of vEMstitch, we conducted thorough testing on simulated datasets involving different transformation combinations, consistently showing promising performance. Furthermore, in real data sample experiments, vEMstitch successfully gives clear ultrastructure in the stitching region, reaffirming the effectiveness of the algorithm.
vEMstitch serves as a valuable tool for large-field and high-resolution image stitching. The clear stitched regions facilitate better visualization and identification in volume EM analysis. The source code is available at https://github.com/HeracleBT/vEMstitch.

Additional details

Github links:

https://github.com/HeracleBT/vEMstitch

Click on a table column to sort the results.

Table Settings

File Name Description Sample ID Data Type File Format Size Release Date File Attributes Download
Readme TEXT 3.33 kB 2024-09-03 MD5 checksum: b4f64f1c9910c3aabbffd81731923ccf
The training and testing images resource for the study. Image archive 4.80 GB 2024-09-03 MD5 checksum: 79bf67f3be108aafbdfdc4dad1a254a3
vEMstitch serves as a valuable tool for large-field and high-resolution image stitching. Download from GitHub repo https://github.com/HeracleBT/vEMstitc on 02-Sepg 2024. This software is released under a GPL-3.0 license. Please visit the GitHub repository for the most recent updates. GitHub archive archive 52.58 MB 2024-09-03 license: GPL-3.0
MD5 checksum: b2c1b8303a3dbfd3a881879542b50743
Funding body Awardee Award ID Comments
National Key Research and Development Program of China 2021YFF0704300
National Key Research and Development Program of China 2020YFA0712401
Chinese Academy of Sciences XDB37010100
National Natural Science Foundation of China Renmin Han 62072280 Projects Grant
National Natural Science Foundation of China Fa Zhang 61932018 Projects Grant
National Natural Science Foundation of China Xiaohua Wan 62072441 Projects Grant
National Natural Science Foundation of China Fa Zhang 31730023 Projects Grant
National Natural Science Foundation of China Fa Zhang 31521002 Projects Grant
Natural Science Foundation of Shandong Province Renmin Han ZR2023YQ057
National Natural Science Foundation of China Yan Zhang 32371248 Projects Grant
Date Action
September 11, 2024 Dataset publish