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19.07.2022 | Original Article

Super-resolution Segmentation Network for Reconstruction of Packed Neurites

verfasst von: Hang Zhou, Tingting Cao, Tian Liu, Shijie Liu, Lu Chen, Yijun Chen, Qing Huang, Wei Ye, Shaoqun Zeng, Tingwei Quan

Erschienen in: Neuroinformatics | Ausgabe 4/2022

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Abstract

Neuron reconstruction can provide the quantitative data required for measuring the neuronal morphology and is crucial in brain research. However, the difficulty in reconstructing dense neurites, wherein massive labor is required for accurate reconstruction in most cases, has not been well resolved. In this work, we provide a new pathway for solving this challenge by proposing the super-resolution segmentation network (SRSNet), which builds the mapping of the neurites in the original neuronal images and their segmentation in a higher-resolution (HR) space. During the segmentation process, the distances between the boundaries of the packed neurites are enlarged, and only the central parts of the neurites are segmented. Owing to this strategy, the super-resolution segmented images are produced for subsequent reconstruction. We carried out experiments on neuronal images with a voxel size of 0.2 μm × 0.2 μm × 1 μm produced by fMOST. SRSNet achieves an average F1 score of 0.88 for automatic packed neurites reconstruction, which takes both the precision and recall values into account, while the average F1 scores of other state-of-the-art automatic tracing methods are less than 0.70.

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Titel: Super-resolution Segmentation Network for Reconstruction of Packed Neurites
verfasst von: Hang Zhou
Tingting Cao
Tian Liu
Shijie Liu
Lu Chen
Yijun Chen
Qing Huang
Wei Ye
Shaoqun Zeng
Tingwei Quan
Publikationsdatum: 19.07.2022
Verlag: Springer US
Erschienen in: Neuroinformatics / Ausgabe 4/2022
Print ISSN: 1539-2791
Elektronische ISSN: 1559-0089
DOI: https://doi.org/10.1007/s12021-022-09594-3

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