Segmentation of brain ultrastructures in 3D electron microscopy

White matter datasets: We prepared ten samples from rats' white matter for serial block-face scanning electron microscopy (SBEM) imaging. The SBEM images were acquired simultaneously at two resolutions, low and high resolutions, from the white matter. The low-resolution datasets were acquired at a large field of view, 200 um x 100 um x 65 um, with a voxel size of 50 nm x 50 nm x 50 nm. Two-thirds of the low-resolution images correspond to the corpus callosum and one-third to the cingulum. The high-resolution datasets were acquired at a small field of view, 15 um x 15 um x 15 um, with a voxel size of 15 nm x 15 nm x 50 nm from the corpus callosum. All the images were acquired from the ipsi- and contralateral hemispheres of sham-operated rats (n = 2) and rats with traumatic brain injury (n = 3). The high-resolution datasets were automatically segmented using ACSON pipeline [1] and the low-resolution datasets using DeepACSON pipeline [2]. Gray matter datasets: From three rats, one sham-operated (n = 1) and two with traumatic brain injury (n = 2), we collected two samples per brain: the ipsilateral and contralateral hemispheres of the layer VI of the primary somatosensory cortex. The high-resolution datasets of the somatosensory cortex were acquired at a small field of view, 15 um x 15 um x 15 um, with a voxel size of 15 nm x 15 nm x 50 nm using the SBEM technique. The high-resolution datasets were automatically segmented using gACSON software [4]. Please, find the "README" file for further explaining details of the data. You can find the "Methods" section in [1-4] for details related to SBEM image acquisition and image segmentation pipelines. In case of questions, please contact the corresponding author, Alejandra Sierra. Please cite the datasets as follows: Abdollahzadeh, A., Belevich, I., Jokitalo, E., Tohka, J. & Sierra, A. Segmentation of brain ultrastructures in 3D electron microscopy (2020). https://doi.org/10.23729/bad417ca-553f-4fa6-ae0a-22eddd29a230 [1] Abdollahzadeh, A., Belevich, I., Jokitalo, E., Tohka, J. & Sierra, A. Automated 3D Axonal Morphometry of White Matter. Sci. Reports 9 (1), 6084 (2019). https://doi.org/10.1038/s41598-019-42648-2. [2] Abdollahzadeh, A., Belevich, I., Jokitalo, E., Sierra, A. & Tohka, J. DeepACSON Automated Segmentation of White Matter in 3D Electron Microscopy, Communications Biology 4(1):179 (2021)(Abdollahzadeh et al. 2021). https://doi.org/10.1038/s42003-021-01699-w [3] Abdollahzadeh, A, Sierra, S, & Tohka, J. Cylindrical Shape Decomposition for 3D Segmentation of Tubular Objects, IEEE Access 9: 23979–95 (2021). https://doi.org/10.1109/ACCESS.2021.3056958. [4] Behanova, A, Abdollahzadeh, A, Belevich, I, Jokitalo, E, Sierra, A, & Tohka, J. gACSON Software for Automated Segmentation and Morphology Analyses of Myelinated Axons in 3D Electron Microscopy, Computer Methods and Programs in Biomedicine 220, 106802 (2022), https://doi.org/10.1016/j.cmpb.2022.106802