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.