The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.
Machine learning analysis of whole mouse brain vasculature
Open accessAbstract
Tissue clearing methods enable the imaging of biological specimens without sectioning. However, reliable and scalable analysis of large imaging datasets in three dimensions remains a challenge. Here we developed a deep learning-based framework to quantify and analyze brain vasculature, named Vessel Segmentation & Analysis Pipeline (VesSAP). Our pipeline uses a convolutional neural network (CNN) with a transfer learning approach for segmentation and achieves human-level accuracy. By using VesSAP, we analyzed the vascular features of whole C57BL/6J, CD1 and BALB/c mouse brains at the micrometer scale after registering them to the Allen mouse brain atlas. We report evidence of secondary intracranial collateral vascularization in CD1 mice and find reduced vascularization of the brainstem in comparison to the cerebrum. Thus, VesSAP enables unbiased and scalable quantifications of the angioarchitecture of cleared mouse brains and yields biological insights into the vascular function of the brain.
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“However, tools for comprehensive, high-resolution analysis of disease-associated changes at the whole-body scale have been lacking.”
From A deep-learning framework reveals whole-body perturbations at cell level (2026) · cites this paper as [9]
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“Although several approaches for image analysis of light-sheet fluorescence microscopy (LSFM) datasets have been introduced in recent years6–10, these methods are typically limited to selected organs and do not provide integrated whole-body quantification of elongated structures.”
From A deep-learning framework reveals whole-body perturbations at cell level (2026) · cites this paper as [9]
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Cites
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
Milletari, F., Navab, N., & Ahmadi, S.-A. (2016) · 2016 Fourth International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2016.79 · Cited by 11,931
Fully convolutional networks for semantic segmentation
Shelhamer, E., Long, J., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2572683 · Cited by 11,898
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., & Ronneberger, O. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46723-8_49 · Cited by 7,945
Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528162 · Cited by 5,759
Elastix: a toolbox for intensity-based medical image registration
Klein, S., Staring, M., Murphy, K., Viergever, M. A., & Pluim, J. (2010) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2009.2035616 · Cited by 4,311
Multiscale vessel enhancement filtering
Frangi, A. F., Niessen, W. J., Vincken, K. L., & Viergever, M. A. (1998) · Lecture notes in computer science · doi:10.1007/bfb0056195 · Cited by 3,916
U-Net: deep learning for cell counting, detection, and morphometry
Falk, T., Mai, D., Bensch, R., Çiçek, Ö., Abdulkadir, A., Marrakchi, Y., Böhm, A., Deubner, J., Jäckel, Z., Seiwald, K., et al. (2018) · Nature Methods · doi:10.1038/s41592-018-0261-2 · Cited by 1,920
Building Skeleton Models via 3-D Medial Surface Axis Thinning Algorithms
Lee, T. C., Kashyap, R. L., & Chu, C.-N. (1994) · CVGIP Graphical Models and Image Processing · doi:10.1006/cgip.1994.1042 · Cited by 1,587
Research works printed but not linked: 5
- Menti, E., Bonaldi, L., Ballerini, L., Ruggeri, A. & Trucco, E. In International Workshop on Simulation and Synthesis in Medical Imaging 167–176 (Springer, Year).
- Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. Preprint at https://arxiv.org/abs/1412.6980 (2017). · arXiv
- Abadi, M. et al. in 12th USENIX Symposium on Operating Systems Design and Implementation 265–283 (2016).
- Paetzold, J. C. et al. clDice—a novel connectivity-preserving loss function for vessel segmentation. in Medical Imaging Meets NeurIPS 2019 Workshop (2019).
- Cohen, J. The effect size index: d. Stat. Power Anal. Behav. Sci. 2, 284–288 (1988).
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Li, C., Leitner, D., Pang, H., Li, A., Bruno, M. T., Faustin, A., Devinsky, O., Wısnıewskı, T., Wadghiri, Y. Z., Zhang, J., et al. (2026) · NMR in Biomedicine · doi:10.1002/nbm.70411
Hu, Y., Wang, X., Sun, P., Shen, Y., & quan, T. (2026) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2026.1925112
Huang, Y.-Y., Patel, R. J. S., Popel, A. S., Bhargava, A., & Pathak, A. P. (2026) · Microcirculation · doi:10.1111/micc.70086
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Liu, T., Xia, W., & Pouliopoulos, A. N. (2026) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2026.10.006
Metadata
- Authors
- Todorov, M. I., Paetzold, J. C., Schoppe, O., Tetteh, G., Shit, S., Efremov, V., Todorov-Völgyi, K., Düring, M., Dichgans, M., Piraud, M., Menze, B., & Ertürk, A.
- Affiliations
- Helmholtz Munich (DE)
- Technical University of Munich (DE)
- University of Zurich (CH)
- German Center for Neurodegenerative Diseases (DE)
- Corresponding author
- Menze, Bjoern
- Ertürk, Ali
- ORCID
- Venue
- Nature Methods
- Published
- 2020-03-11
- Volume
- 17
- Issue
- 4
- Pages
- 442-449
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC7591801
- OpenAlex
- W3012387312
- Cited by
- 338
- References
- 51
- Citations by year
- 2026: 32 · 2025: 45 · 2024: 69 · 2023: 54 · 2022: 56 · 2021: 61 · 2020: 20
- FWCI
- 18.56
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Methods (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Open access LMU (Ludwid Maxmilian's Universitat Munchen) (submittedVersion)
- Site cant be reached (submittedVersion)
- DZNE Pub (submittedVersion)
- PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion)
- Funders
- National Institute on Aging (RF1AG057575)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Keywords
- cerebral vasculature, vessel segmentation, tissue clearing, whole-brain imaging, convolutional neural networks, transfer learning, Allen Mouse Brain Atlas, intracranial collateral circulation, mouse brain, angioarchitecture
- MeSH terms
- Machine Learning, Animals, Brain, Imaging, Three-Dimensional, Mice
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