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idr0147-study.txt
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idr0147-study.txt
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# FILL IN AS MUCH INFORMATION AS YOU CAN. HINTS HAVE BEEN PUT IN SOME FIELDS AFTER THE HASH # SYMBOL. REPLACE THE HINT WITH TEXT WHERE APPROPRIATE.
# STUDY DESCRIPTION SECTION
# Section with generic information about the study including title, description, publication details (if applicable) and contact details
Comment[IDR Study Accession] idr0147
Study Title Terabyte-scale supervised 3D training and benchmarking dataset of the mouse kidney
Study Type machine learning
Study Type Term Source REF OBI
Study Type Term Accession 0002587
Study Description The performance of machine learning algorithms, when used for segmenting 3D biomedical images, does not reach the level expected based on results achieved with 2D photos. This may be explained by the comparative lack of high-volume, high-quality training datasets, which require state-of-the art imaging facilities, domain experts for annotation and large computational and personal resources. The HR-Kidney dataset presented in this work bridges this gap by providing 1.7 TB of artefact-corrected synchrotron radiation-based X-ray phase-contrast microtomography images of whole mouse kidneys and validated segmentations of 33 729 glomeruli, which corresponds to a one to two orders of magnitude increase over currently available biomedical datasets. The image sets also contain the underlying raw data, threshold- and morphology-based semi-automatic segmentations of renal vasculature and uriniferous tubules, as well as true 3D manual annotations. We therewith provide a broad basis for the scientific community to build upon and expand in the fields of image processing, data augmentation and machine learning, in particular unsupervised and semi-supervised learning investigations, as well as transfer learning and generative adversarial networks.
Study Key Words kidney glomeruli blood vessels tubules whole organ imaging mouse computed tomography propagation-based phase contrast synchrotron vascular casting micrometer resolution contrast agent machine learning segmentation training data scattering transform terabyte scale data manual annotation
Study Organism Mus musculus
Study Organism Term Source REF NCBITaxon
Study Organism Term Accession 10090
Study Experiments Number 1
Study External URL
Study BioImage Archive Accession
Study Public Release Date 2023-06-19
# Study Publication
Study PubMed ID 37537174
Study Publication Title Terabyte-scale supervised 3D training and benchmarking dataset of the mouse kidney
Study Author List Kuo W, Rossinelli D, Schulz G, Wenger RH, Hieber S, Müller B, Kurtcuoglu V
Study PMC ID PMC10400611
Study DOI https://doi.org/10.1038/s41597-023-02407-5
# Study Contacts
Study Person Last Name Kurtcuoglu Kuo
Study Person First Name Vartan Willy
Study Person Email vartan.kurtcuoglu@uzh.ch willy.kuo@uzh.ch
Study Person Address Institute of Physiology, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland Institute of Physiology, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
Study Person ORCID 0000-0003-2665-0995 0000-0002-0870-7997
Study Person Roles corresponding author submitter
# Study License and Data DOI
Study License CC BY 4.0
Study License URL https://creativecommons.org/licenses/by/4.0/
Study Copyright Kuo at al
Study Data Publisher University of Dundee
Study Data DOI https://doi.org/10.17867/10000188
Term Source Name NCBITaxon EFO CMPO FBbi
Term Source URI http://purl.obolibrary.org/obo/ http://www.ebi.ac.uk/efo/ http://www.ebi.ac.uk/cmpo/ http://purl.obolibrary.org/obo/
# EXPERIMENT SECTION
# Experiment Section containing all information relative to each experiment in the study including materials used, protocols names and description, phenotype names and description. For multiple experiments this section should be repeated. Copy and paste the whole section below and fill out for the next experiment
Experiment Number 1
Comment[IDR Experiment Name] idr0147-kuo-kidney3d/experimentA
Experiment Sample Type tissue
Experiment Description C57BL/6J mice were purchased from Janvier Labs (Le Genest-Saint-Isle, France) and kept in individually ventilated cages with ad libitum access to water and standard diet (Kliba Nafag 3436, Kaiseraugst, Switzerland) in 12 h light/dark cycles. Dataset 1 derives from the left kidney of a male mouse, 15 weeks of age with a body weight of 28.0 g. Dataset 2 is the right kidney of the same mouse. Dataset 3 derives from the right kidney of a female mouse, 15 weeks of age with a body weight of 22.5 g. All animal experiments were approved by the cantonal veterinary office of Zurich, Switzerland, in accordance with the Swiss federal animal welfare regulations (license numbers ZH177/13 and ZH233/15). Mice were anaesthetized with ketamine/xylazine. A blunted 21G butterfly needle was inserted retrogradely into the abdominal aorta and fixed with a ligation. The abdominal aorta and superior mesenteric artery above the renal arteries were ligated, the vena cava opened as an outlet and the kidneys were flushed with 10 ml, 37 °C phosphate-buffered saline (PBS) to remove the blood, then fixed with 50 ml 37 °C 4 % paraformaldehyde in PBS (PFA) solution at 150 mmHg hydrostatic pressure. 2.4 g of 1,3-diiodobenzene (Sigma-Aldrich, Schnelldorf, Germany) were dissolved in 7.5 g of 2-butanone (Sigma-Aldrich) and mixed with 7.5 g PU4ii resin (vasQtec, Zurich, Switzerland) and 1.3 g PU4ii hardener. The mixture was filtered through a paper filter and degassed extensively in a vacuum chamber to minimize bubble formation during polymerization, and perfused at a constant pressure of no more than 200 mmHg until the resin mixture solidified. Kidneys were excised and stored in 15 ml 4 % PFA. For scanning, they were embedded in 2 % agar in PBS in 0.5 ml polypropylene centrifugation tubes. Kidneys were quality-checked with a nanotom® m (phoenix|x-ray, GE Sensing & Inspection Technologies GmbH, Wunstorf, Germany). Samples showing insufficient perfusion or bleeding of resin into the renal capsule or sinuses were excluded. Kidneys were scanned at the ID19 tomography beamline of the European Synchrotron Radiation Facility (ESRF, Grenoble, France) using pink beam with a mean photon energy of 19 keV. Radiographs were recorded at a sample-detector distance of 28 cm with a 100 µm Ce:LuAG scintillator, 4× magnification lens and a pco.edge 5.5 camera with a 2560 × 2160 pixel array and 6.5 µm pixel size, resulting in an effective pixel size of 1.625 µm. Radiographs were acquired with a half-acquisition scheme in order to extend the field of view to 8 mm. Six height steps were recorded for each kidney, with half of the vertical field of view overlapping between each height step, resulting in fully redundant acquisition of the inner height steps. 5125 radiographs were recorded for each height step with 0.1 s exposure time, resulting in a scan time of 1 h for a whole kidney. 100 flat-field images were taken before and after each height step for flat-field correction. Images were reconstructed using the beamline’s in-house PyHST2 software, using a Paganin-filter with a low δ/β ratio of 50 to limit loss in resolution and appearance of gradients close to large vessels. Registration for stitching two half-acquisition radiographs to the full field of view was performed manually with 1 pixel accuracy. Data size for the reconstructed datasets was 1158 GB per kidney. Outliers in intensity in the recorded flat fields were segmented by noise reduction with 2D continuous curvelets, followed by thresholding to calculate radius and coordinates of the ring artefacts. The redundant acquisition of the central four height steps allowed us to replace corrupted data with a weighted average during stitching. The signals of the individual slices were zeroed in the presence of the rings, summed up and normalized by counting the number of uncorrupted signals. In the outer slices, where no redundant data was available, and in locations where rings coincided in both height steps, we employed a discrete cosine transform-based inpainting technique with a simple iterative approach, where we picked smoothing kernels progressively smaller in size and reconstructed the signal in the target areas by smoothing the signal everywhere at each iteration. The smoothed signal in the target areas was then combined with the original signal elsewhere to form a new image. In the next iteration, in turn, the new image was then smoothed to rewrite the signal at the target regions. The final inpainted signal exhibits multiple scales since different kernel widths are considered at different iterations. The alignment for stitching the six stacks was determined by carrying out manual 3D registration and double checking against pairwise stack-stack phase-correlation analysis. The stitching process reduced the dataset dimensions per kidney to 4608 × 4608 × 7168 pixels, totaling 567 GB. We performed image enhancement based on 3D discretized continuous curvelets, in a similar fashion as Starck et al., but with second generation curvelets (i.e., no Radon transform) in 3D. The enhancement was carried out globally by leveraging the Fast Fourier Transform with MPI-FFTW, considering about 100 curvelets. The “wedges” (curvelets in the spectrum) have a conical shape and cover the unit sphere in an approximately uniform fashion. For a given curvelet, a per-pixel coefficient is obtained by computing an inverse Fourier transform of its wedge and the image spectrum. We then truncated these coefficients in the image domain against a hard threshold, and forward-transformed the curvelet again into the Fourier space, modulated the curvelets with the truncated coefficients and superposed them. As a result, the pixel intensities were compressed to a substantially smaller range of values, thus helping to avoid over- and under-segmentation of large and small vessels, respectively. A threshold-based segmentation followed the image enhancement. The enhancement parameters and threshold were manually chosen by examining six randomly chosen regions of interest. Spurious islands were removed by 26-connected component analysis, and cavities were removed by 6-connected component analysis. The bulk of the processing workload, required to transform data into an actionable training set, was carried out at the Zeus cluster of the Pawsey supercomputing centre. Zeus consisted of hundreds of computing nodes featuring Intel Xeon Phi (Knights Landing) many-core CPUs, together with 96 GB of ``special’’ high-bandwidth memory (HBM/MCDRAM), as well as 128 GB of conventional DDR4 RAM. The final training and assessments were carried out at the Euler VI cluster of ETH Zurich, with two-socket nodes featuring AMD EPYC 7742 (Rome) CPUs and 512 GB of DDR4 RAM. A machine learning-based approach relying on invariant scattering convolution networks was employed to segment the glomeruli and remove perirenal fat from the blood vessel segment. For the glomerular training data, three selected regions of interest of 512 × 256 × 256 voxels in size were selected from the cortical region of one kidney (dataset 2) and segmented by a single annotator by fully manual contouring in all slices. For the fat, manual work was reduced by providing an initial semiautomatic segmentation, which the manual annotation then corrected. The training data were supplemented by additional regions of interest that contained no glomeruli or fat at all, and thus did not require manual annotation. The manual annotations were then used to train a hybrid algorithm that relied on a 3D scattering transform convolutional network topped with a dense neural network. The scattering transform relied upon ad-hoc designed 3D kernels (Morlet’s wavelet with different sizes and orientations) that uniformly covered all directions at different scales. In the scattering convolutional network, filter nonlinearities were obtained by taking the magnitude of the filter responses and convolving them again with the kernels in a cascading fashion. These nonlinearities are designed to be robust against small Lipschitz-continuous deformations of the image. In contrast to our curvelet-based image enhancement approach, we decomposed the image into cubic tiles, then applied a windowed (thus local) Fourier transform on the tiles by considering regions about twice their size around them. While it would have been possible to use a convolutional network based upon a global scattering transform, this would have produced a very large number of features that would have had to be consumed at once, leading to an intermediate footprint in the petabyte-scale, exceeding the available memory of the cluster. The scattering transform convolutional network produced a stack of a few hundred scalar feature maps per pixel. If considered as a “fiber bundle”, the feature map stack is equivariant under the symmetry group of rotations (i.e., the stack is a regular representation of the 3D rotation group SO(3)). This property can be exploited by further processing the feature maps with a dense neural network with increased parameter sharing across the hidden layers, making the output layer-invariant to rotations.
Experiment Size 5D Images: Average Image Dimension (XYZCT): 4608 x 4608 x 7168 x 1 x 1 Total Tb:
Experiment Example Images
Experiment Imaging Method X-Ray Microtomography
Experiment Imaging Method Term Source REF OMIT
Experiment Imaging Method Term Accession 0026155
Experiment Organism
Experiment Organism Term Source REF NCBITaxon
Experiment Organism Term Accession
Experiment Comments synchrotron radiation-based X-ray phase-contrast microtomography (SRµCT)
# assay files
Experiment Assay File idr0147-experimentA-annotation
Experiment Assay File Format tab-delimited text
Assay Experimental Conditions
Assay Experimental Conditions Term Source REF
Assay Experimental Conditions Term Accession
Quality Control Description
# Protocols
Protocol Name growth protocol treatment protocol image acquisition and feature extraction protocol data analysis protocol
Protocol Type growth protocol treatment protocol image acquisition and feature extraction protocol data analysis protocol
Protocol Type Term Source REF EFO EFO
Protocol Type Term Accession EFO_0003789 EFO_0003969
Protocol Description
# Phenotypes
Phenotype Name
Phenotype Description
Phenotype Score Type
Phenotype Term Source REF CMPO
Phenotype Term Name
Phenotype Term Accession
# Feature Level Data Files (give individual file details unless there is one file per well)
Feature Level Data File Name
Feature Level Data File Format
Feature Level Data File Description
Feature Level Data Column Name
Feature Level Data Column Description
# Processed Data Files
Processed Data File Name
Processed Data File Format tab-delimited text
Processed Data File Description
Processed Data Column Name
Processed Data Column Type
Processed Data Column Annotation Level
Processed Data Column Description
Processed Data Column Link To Assay File