A high-performance deep-learning-based pipeline for whole-brain vasculature segmentation at the capillary resolution Article Swipe
Yuxin Li
,
Xuhua Liu
,
Xueyan Jia
,
Tao Jiang
,
Jianghao Wu
,
Qianlong Zhang
,
Junhuai Li
,
Xiangning Li
,
Anan Li
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1093/bioinformatics/btad145
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1093/bioinformatics/btad145
Motivation Reconstructing and analyzing all blood vessels throughout the brain is significant for understanding brain function, revealing the mechanisms of brain disease, and mapping the whole-brain vascular atlas. Vessel segmentation is a fundamental step in reconstruction and analysis. The whole-brain optical microscopic imaging method enables the acquisition of whole-brain vessel images at the capillary resolution. Due to the massive amount of data and the complex vascular features generated by high-resolution whole-brain imaging, achieving rapid and accurate segmentation of whole-brain vasculature becomes a challenge. Results We introduce HP-VSP, a high-performance vessel segmentation pipeline based on deep learning. The pipeline consists of three processes: data blocking, block prediction, and block fusion. We used parallel computing to parallelize this pipeline to improve the efficiency of whole-brain vessel segmentation. We also designed a lightweight deep neural network based on multi-resolution vessel feature extraction to segment vessels at different scales throughout the brain accurately. We validated our approach on whole-brain vascular data from three transgenic mice collected by HD-fMOST. The results show that our proposed segmentation network achieves the state-of-the-art level under various evaluation metrics. In contrast, the parameters of the network are only 1% of those of similar networks. The established segmentation pipeline could be used on various computing platforms and complete the whole-brain vessel segmentation in 3 h. We also demonstrated that our pipeline could be applied to the vascular analysis. Availability and implementation The dataset is available at http://atlas.brainsmatics.org/a/li2301. The source code is freely available at https://github.com/visionlyx/HP-VSP.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/bioinformatics/btad145
- https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btad145/49590991/btad145.pdf
- OA Status
- gold
- Cited By
- 6
- References
- 22
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4328143759
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4328143759Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/bioinformatics/btad145Digital Object Identifier
- Title
-
A high-performance deep-learning-based pipeline for whole-brain vasculature segmentation at the capillary resolutionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-03-22Full publication date if available
- Authors
-
Yuxin Li, Xuhua Liu, Xueyan Jia, Tao Jiang, Jianghao Wu, Qianlong Zhang, Junhuai Li, Xiangning Li, Anan LiList of authors in order
- Landing page
-
https://doi.org/10.1093/bioinformatics/btad145Publisher landing page
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https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btad145/49590991/btad145.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btad145/49590991/btad145.pdfDirect OA link when available
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Segmentation, Computer science, Pipeline (software), Artificial intelligence, Deep learning, Block (permutation group theory), Brain atlas, Computer vision, Pattern recognition (psychology), Programming language, Mathematics, GeometryTop concepts (fields/topics) attached by OpenAlex
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6Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 1, 2023: 2Per-year citation counts (last 5 years)
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22Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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