Dataset for: Quantification of vascular networks in photoacoustic mesoscopy
Repository URI
Repository DOI
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Type
Dataset
Change log
Authors
Brown, Emma https://orcid.org/0000-0002-2153-2992
Lefebvre, Thierry
Sweeney, Paul
Stolz, Bernadette
Groehl, Janek
Description
The zip files contain the following data:
In the InSilico folder,
- RawLnet: Raw binary mask of an examplar L-System
- OpticalSimLnet: Intermediate L-Net post forward optical simulation
- AcousticSimLnet: Reconstructed L-Net post acoustic simulation
- FinalDenoisedLnet: Final denoised L-Net used for analysis
- VesselnessFilteredLNet: (optional) Vesselness filtered final L-Net
- 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,
- RawPhantom: Raw reconstructed string image exported from the RSOM
- FinalDenoisedPhantom: Final denoised string image used for analysis
- VesselnessFilteredPhantom: (optional) Vesselness filtered string image
- 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,
- RawRSOM: Raw reconstructed tumour image exported from the RSOM
- FinalDenoisedRSOM: Final denoised tumour image used for analysis
- VesselnessFilteredRSOM: (optional) Vesselness filtered tumour image
- SegmentedRSOM: Final segmented tumour image using the four proposed methods (auto-thresholding, auto-thresholding + vesselness filtering, random forest, random forest + vesselness filtering)
Version
Software / Usage instructions
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
Publisher
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)
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)