3D Microvascular Image Data and Labels for Machine Learning

<p dir="ltr">These images and associated binary labels were collected from collaborators across multiple universities to serve as a diverse representation of biomedical images of vessel structures, for use in the training and validation of machine learning tools for vessel segmentation. The dataset contains images from a variety of imaging modalities, at different resolutions, using difference sources of contrast and featuring different organs/ pathologies. This data was use to train, test and validated a foundational model for 3D vessel segmentation, tUbeNet, which can be found on <a href="https://github.com/natalie11/tUbeNet" rel="noreferrer" target="_blank">github</a>. The paper descripting the training and validation of the model can be found <a href="https://doi.org/10.1101/2023.07.24.550334" rel="noreferrer" target="_blank">here</a>. </p><p dir="ltr">Filenames are structured as follows:  <br>Data - [Modality]_[species Organ]_[resolution].tif </p><p dir="ltr">Labels - [Modality]_[species Organ]_[resolution]_labels.tif <br>Sub-volumes of larger dataset - [Modality]_[species Organ]_subvolume[dimensions in pixels].tif </p><p dir="ltr"><br>Manual labelling of blood vessels was carried out using Amira (2020.2, Thermo-Fisher, UK).   </p><p><br></p><p dir="ltr"><b>Training data:</b><b> </b></p><ol><li>opticalHREM_murineLiver_2.26x2.26x1.75um.tif: A high resolution episcopic microscopy (HREM) dataset, acquired in house by staining a healthy mouse liver with Eosin B and imaged using a standard HREM protocol.  <i>NB: </i><i>25% of this image volume was with</i><i>h</i><i>e</i><i>l</i><i>d from training, for use as test data</i><i>.</i><i> </i></li></ol><ol><li>CT_murineTumour_20x20x20um.tif: X-ray microCT images of a microvascular cast, taken from a subcutaneous mouse model of colorectal cancer (acquired in house). <i>NB: </i><i>25% of this image volume was </i><i>withhe</i><i>ld</i><i> from training, for use as test data</i><i>.</i><i> </i></li></ol><ol><li>RSOM_murineTumour_20x20um.tif: Raster-Scanning Optoacoustic Mesoscopy (RSOM) data from a subcutaneous tumour model (provided by Emma Brown, Bohndiek Group, University of Cambridge). The image data has undergone filtering to reduce the background ​(Brown et al., 2019)​.  </li></ol><ol><li>OCTA_humanRetina_24x24um.tif: retinal angiography data obtained using Optical Coherence Tomography Angiography (OCT-A) (provided by Dr Ranjan Rajendram, Moorfields Eye Hospital). </li></ol><p dir="ltr"><b>Test data:</b><b> </b></p><ol><li>MRI_porcineLiver_0.9x0.9x5mm.tif: T1-weighted Balanced Turbo Field Echo Magnetic Resonance Imaging (MRI) data from a machine-perfused porcine liver, acquired in-house. </li></ol><p dir="ltr"><b>Test Data</b><b> </b></p><ol><li>MFHREM_murineTumourLectin_2.76x2.76x2.61um.tif: a subcutaneous colorectal tumour mouse model was imaged in house using Multi-fluorescence HREM in house, with Dylight 647 conjugated lectin staining the vasculature ​(Walsh et al., 2021)​. The image data has been processed using an asymmetric deconvolution algorithm described by ​Walsh et al., 2020​. NB:<i> </i><i>A sub-volume of 480x480x640 voxels was manually labelled (</i><i>MFHREM_murineTumourLectin_subvolume480x480x640</i><i>.tif</i><i>).</i><i> </i></li></ol><ol><li> MFHREM_murineBrainLectin_0.85x0.85x0.86um.tif: an MF-HREM image of the cortex of a mouse brain, stained with Dylight-647 conjugated lectin, was acquired in house ​(Walsh et al., 2021)​. The image data has been downsampled and processed using an asymmetric deconvolution algorithm described by ​Walsh et al., 2020​. <i>NB: </i><i>A sub-volume of </i><i>1000x1000x99</i><i> voxels was manually labelled.</i><i> This sub-volume is provided at full resolution and without preprocessing (</i><i>MFHREM_murineBrainLectin_subvol_0.57x0.57x0.86um</i><i>.tif</i><i>).</i><i> </i></li></ol><ol><li> 2Photon_murineOlfactoryBulbLectin_0.2x0.46x5.2um.tif: two-photon data of mouse olfactory bulb blood vessels, labelled with sulforhodamine 101, was kindly provided by Yuxin Zhang at the Sensory Circuits and Neurotechnology Lab, the Francis Crick Institute ​(Bosch et al., 2022)​.  <i>NB: </i><i>A sub-volume of 500x500x79 voxel was manually labelled (</i><i>2Photon_murineOlfactoryBulbLectin_subvolume500x500x79</i><i>.tif</i><i>).</i><i> </i></li></ol><p><br></p><p dir="ltr"><b>References</b><b>:</b><b> </b></p><p dir="ltr">​​Bosch, C., Ackels, T., Pacureanu, A., Zhang, Y., Peddie, C. J., Berning, M., Rzepka, N., Zdora, M. C., Whiteley, I., Storm, M., Bonnin, A., Rau, C., Margrie, T., Collinson, L., & Schaefer, A. T. (2022). Functional and multiscale 3D structural investigation of brain tissue through correlative in vivo physiology, synchrotron microtomography and volume electron microscopy. Nature Communications 2022 13:1, 13(1), 1–16. <a href="https://doi.org/10.1038/s41467-022-30199-6" rel="noreferrer" target="_blank">https://doi.org/10.1038/s41467-022-30199-6 </a></p><p dir="ltr">​Brown, E., Brunker, J., & Bohndiek, S. E. (2019). Photoacoustic imaging as a tool to probe the tumour microenvironment. DMM Disease Models and Mechanisms, 12(7). <a href="https://doi.org/10.1242/DMM.039636" rel="noreferrer" target="_blank">https://doi.org/10.1242/DMM.039636</a> </p><p dir="ltr">​Walsh, C., Holroyd, N. A., Finnerty, E., Ryan, S. G., Sweeney, P. W., Shipley, R. J., & Walker-Samuel, S. (2021). Multifluorescence High-Resolution Episcopic Microscopy for 3D Imaging of Adult Murine Organs. Advanced Photonics Research, 2(10), 2100110. <a href="https://doi.org/10.1002/ADPR.202100110" rel="noreferrer" target="_blank">https://doi.org/10.1002/ADPR.202100110</a> </p><p dir="ltr">​Walsh, C., Holroyd, N., Shipley, R., & Walker-Samuel, S. (2020). Asymmetric Point Spread Function Estimation and Deconvolution for Serial-Sectioning Block-Face Imaging. Communications in Computer and Information Science, 1248 CCIS, 235–249. <a href="https://doi.org/10.1007/978-3-030-52791-4_19" rel="noreferrer" target="_blank">https://doi.org/10.1007/978-3-030-52791-4_19</a> </p><p>​ </p>