Dataset for: Quantification of vascular networks in photoacoustic mesoscopy
Authors
Lefebvre, Thierry
Sweeney, Paul
Stolz, Bernadette
Groehl, Janek
Hacker, Lina
Huang, Ziqiang
Couturier, Dominique-Laurent
Harrington, Heather
Byrne, Heather
Publication Date
2022-06-08Type
Dataset
Metadata
Show full item recordCitation
Brown, E., Lefebvre, T., Sweeney, P., Stolz, B., Groehl, J., Hacker, L., Huang, Z., et al. (2022). Dataset for: Quantification of vascular networks in photoacoustic mesoscopy [Dataset]. https://doi.org/10.17863/CAM.78208
Description
The zip files contain the following data:
In the InSilico folder,
1. RawLnet: Raw binary mask of an examplar L-System
2. OpticalSimLnet: Intermediate L-Net post forward optical simulation
3. AcousticSimLnet: Reconstructed L-Net post acoustic simulation
4. FinalDenoisedLnet: Final denoised L-Net used for analysis
5. VesselnessFilteredLNet: (optional) Vesselness filtered final L-Net
6. SegmentedLNet: Final segmented L-Net using the four proposed methods (auto-thresholding, auto-thresholding + vesselness filtering, random forest, random forest + vesselness filtering)
In the Phantom folder,
1. RawPhantom: Raw reconstructed string image exported from the RSOM
2. FinalDenoisedPhantom: Final denoised string image used for analysis
3. VesselnessFilteredPhantom: (optional) Vesselness filtered string image
4. SegmentedPhantom: Final segmented string image using the four proposed methods (auto-thresholding, auto-thresholding + vesselness filtering, random forest, random forest + vesselness filtering)
In the InVivo folder,
1. RawRSOM: Raw reconstructed tumour image exported from the RSOM
2. FinalDenoisedRSOM: Final denoised tumour image used for analysis
3. VesselnessFilteredRSOM: (optional) Vesselness filtered tumour image
4. SegmentedRSOM: Final segmented tumour image using the four proposed methods (auto-thresholding, auto-thresholding + vesselness filtering, random forest, random forest + vesselness filtering)
Format
The software associated with the deposited data is a series of custom codes that are hosted on publicly available repositories.
Code to generate synthetic vascular trees (LNets) is available on GitHub (https://github.com/psweens/V-System).
In silico photoacoustic simulations were performed using the SIMPA toolkit (https://github.com/CAMI-DKFZ/simpa).
Both the trained 3D CNN to extract tumour ROIs from RSOM images (https://github.com/psweens/Predict-RSOM-ROI) and vascular TDA package are available on GitHub (https://github.com/psweens/Vascular-TDA).
Keywords
photoacoustic, vessel networks, topological data analysis, mesoscopy
Relationships
Sponsorship
Cancer Research UK (C14303/A17197)
Engineering and Physical Sciences Research Council (EP/N014588/1)
Cancer Research UK (C14303/A17197)
National Physical Laboratory (NPL) (unknown)
National Physical Laboratory (NPL) (unknown)
National Physical Laboratory (NPL) (unknown)
Cancer Research UK (C47594/A29448)
Identifiers
This record's DOI: https://doi.org/10.17863/CAM.78208
Rights
Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
Licence URL: https://creativecommons.org/licenses/by-nc/4.0/
Statistics
Total file downloads (since January 2020). For more information on metrics see the
IRUS guide.
Recommended or similar items
The current recommendation prototype on the Apollo Repository will be turned off on 03 February 2023. Although the pilot has been fruitful for both parties, the service provider IKVA is focusing on horizon scanning products and so the recommender service can no longer be supported. We recognise the importance of recommender services in supporting research discovery and are evaluating offerings from other service providers. If you would like to offer feedback on this decision please contact us on: support@repository.cam.ac.uk