2024-03-28T16:21:41Z
https://rodare.hzdr.de/oai2d
oai:rodare.hzdr.de:1381
2022-08-10T12:38:36Z
openaire_data
user-health
user-rodare
Moldovan, Rares-Petru
Gündel, Daniel
Deuther-Conrad, Winnie
Ueberham, Lea
Kaur, Sarandeep
Otikova, Elina
Teodoro, Rodrigo
Lai, Thu Hang
Clauß, Oliver
Scheunemann, Matthias
Bormans, Guy
Kopka, Klaus
Bachmann, Michael
Brust, Peter
2022-08-09
The cannabinoid receptor type 2 (CB2R) is an attractive target for diagnosis and therapy of neurodegenerative diseases and cancer. Recently, we reported a novel naphthyrid-2-one based positron-emission tomography (PET) radioligand for imaging of the CB2R in the brain ([18F]5). In this study we aimed at the development of a novel 18F-labeled CB2R radioligand with improved binding properties and metabolic stability. Starting from the structure of 5, we developed a novel series of fluorinated derivatives by modifying the substituents at the naphthyrid-2-one subunit. Compound 28 (LU13) was identified with the highest binding affinity and selectivity versus CB1R (CB2RKi = 0.6 nM; CB1RKi/CB2RKi > 1000) and was selected for radiolabeling with 18F and biological characterization. The radiofluorination was performed starting from the corresponding bromo-precursor (31) bearing a fully deuterated N-alkyl chain to protect against defluorination. The in vitro evaluation of [18F]LU13 proved the high binding affinity of the radioligand towards rat (rCB2RKD = 0.2 nM) and human (hCB2RKD = 1.1 nM) CB2R. Metabolism studies in mice revealed a metabolic stability at 30 min p.i. with fractions of parent compound of >80% in the brain and 90% in the spleen with only trace of defluorination products detected in plasma. PET imaging in a rat model of vector-based/related overexpression in the striatum revealed a high signal to background ratio, demonstrating the ability of [18F]LU13 to reach and selectively label the hCB2R in the brain. Thus, [18F]LU13 is a novel and highly promising PET radioligand for the imaging of up regulated CB2R expression under pathological conditions in the brain.
https://rodare.hzdr.de/record/1381
10.14278/rodare.1381
oai:rodare.hzdr.de:1381
url:https://www.hzdr.de/publications/Publ-34030
url:https://www.hzdr.de/publications/Publ-33987
doi:10.14278/rodare.1380
url:https://rodare.hzdr.de/communities/health
url:https://rodare.hzdr.de/communities/rodare
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/4.0/legalcode
cannabinoid receptor type 2
naphthyrid-2-one
binding affinity
Data Publication: Structure-Based Design, Optimization and Development of [18F]LU13, a novel radioligand for CB2R Imaging in the Brain with PET
info:eu-repo/semantics/other
dataset
oai:rodare.hzdr.de:2381
2023-08-02T06:50:41Z
openaire_data
user-health
user-rodare
user-hzdr
Metzkes-Ng, Josefine
Brack, Florian-Emanuel
Kroll, Florian
Bernert, Constantin
Bock, Stefan
Bodenstein, Elisabeth
Brand, Michael
Cowan, Thomas
Gebhardt, René
Hans, Stefan
Helbig, Uwe
Horst, Felix Ernst
Jansen, Jeannette
Kraft, Stephan
Krause, Mechthild
Leßmann, Elisabeth
Löck, Steffen
Pawelke, Jörg
Püschel, Thomas
Reimold, Marvin
Rehwald, Martin
Richter, Christian
Schlenvoigt, Hans-Peter
Schramm, Ulrich
Schürer, Michael
Seco, Joao
Szabó, Emília Rita
Umlandt, Marvin Elias Paul
Zeil, Karl
Ziegler, Tim
Beyreuther, Elke
2023-07-24
Source data, scripts and parts of figures to generate the figures in publication
https://rodare.hzdr.de/record/2381
10.14278/rodare.2381
oai:rodare.hzdr.de:2381
url:https://www.hzdr.de/publications/Publ-37304
url:https://www.hzdr.de/publications/Publ-37303
doi:10.14278/rodare.2380
url:https://rodare.hzdr.de/communities/health
url:https://rodare.hzdr.de/communities/hzdr
url:https://rodare.hzdr.de/communities/rodare
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/4.0/legalcode
Laser-Plasma Acceleration
FLASH
Radiobiology
Laser-driven proton acceleration
TNSA
UHDR
Ultra-high dose rate
Cancer
Radiotherapy
Data publication: The DRESDEN PLATFORM – A Research Hub for Ultra-high Dose Rate Radiobiology
info:eu-repo/semantics/other
dataset
oai:rodare.hzdr.de:1257
2021-11-12T13:04:56Z
openaire_data
user-health
user-rodare
Moldovan, Rares-Petru
Gündel, Daniel
Teodoro, Rodrigo
Ludwig, Friedrich-Alexander
Fischer, Steffen
Toussaint, Magali
Schepmann, Dirk
Wünsch, Bernhard
Brust, Peter
Deuther-Conrad, Winnie
2021-11-09
The σ2 receptor (transmembrane protein 97), which is involved in cholesterol homeostasis, is of high relevance for neoplastic processes. The upregulated expression of σ2 receptors in cancer cells and tissue in combination with the antiproliferative potency of σ2 receptor ligands motivates the research in the field of 2 receptors for the diagnosis and therapy of different types of cancer. Starting from the well described 2-(4-(1H-indol-1-yl)butyl)-6,7-dimethoxy-1,2,3,4-tetrahydroisoquinoline class of compounds, we synthesized a novel series of fluorinated derivatives, bearing the F-atom at the aromatic indole/azaindole subunit. RM273 (2-[4-(6-fluoro-1H-pyrrolo[2,3-b]pyridin-1-yl)butyl]-6,7-dimethoxy-1,2,3,4-tetrahydroisoquinoline) was selected for labelling with 18F and evaluation regarding detection of σ2 receptors in the brain by positron emission tomography. Initial metabolism and biodistribution studies of [18F]RM273 in healthy mice revealed promising penetration of the radioligand into the brain. Preliminary in vitro autoradiography on brain cryosections of an orthotopic rat glioblastoma model proved the potential of the radioligand to detect the upregulation of σ2 receptor in glioblastoma cells compared to healthy brain. The results indicate that the herein developed σ2 receptor ligand [18F]RM273 has potential to assess by non-invasive molecular imaging the correlation between the availability of σ2 receptors with properties of brain tumors such as tumor proliferation or resistance towards particular therapies
https://rodare.hzdr.de/record/1257
10.14278/rodare.1257
oai:rodare.hzdr.de:1257
url:https://www.hzdr.de/publications/Publ-32485
url:https://www.hzdr.de/publications/Publ-33346
doi:10.14278/rodare.1256
url:https://rodare.hzdr.de/communities/health
url:https://rodare.hzdr.de/communities/rodare
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/4.0/legalcode
σ2 receptor
transmembrane protein 97
azaindoles
binding affinity
radiochemistry
fluorine-18 labeling
positron emission tomography (PET)
brain-penetration
glioblastoma
orthotopic
Data publication: Design, radiosynthesis and preliminary biological evaluation in mice of a brain-penetrant 18F-labelled σ2 receptor ligand
info:eu-repo/semantics/other
dataset
oai:rodare.hzdr.de:801
2023-05-30T13:11:26Z
openaire_data
user-ecfunded
user-health
user-rodare
Müller, Johannes
Suckert, Theresa
Beyreuther, Elke
Schneider, Moritz
Boucsein, Marc
Bodenstein, Elisabeth
Stolz-Kieslich, Liane
Krause, Mechthild
von Neubeck, Cläre
Haase, Robert
Lühr, Armin
Dietrich, Antje
2021-01-20
The dataset contains comprehensive image data for a total of nine mice, which underwent normal tissue brain irradiation with 90 MeV protons.
In particular, the image data comprise cone-bem computed tomographies (CBCT), Monte Carlo beam transport simulations based on those CTs, regular magnetic resonance imaging (MRI) follow-up (≥ 26 weeks), a co-aligned DSURQE mouse brain atlas and scanned whole-brain tissue sections with histochemical and immunofluorescent markers for morphology (H&E), cell nuclei (DAPI), astrocytes (GFAP), microglia (Iba1), the intermediate filament protein Nestin, proliferation (Ki67), neurons (NeuN) and oligodendrocytes (OSP).
The volumetric image data (i.e. CBCT, MRI and brain atlas) were co-aligned using the ImageJ plugin Big Warp. The CBCT data was used as spatial reference to allow for mask-based, slice-wise alignment of CBCT and light microscopy image data in 3D with the scriptable registration tool Elastix.
We provide the data in raw format and as aligned data sets, as well as their spatial transformations.
https://rodare.hzdr.de/record/801
10.14278/rodare.801
oai:rodare.hzdr.de:801
eng
info:eu-repo/grantAgreement/EC/H2020/730983/
url:https://www.hzdr.de/publications/Publ-32124
doi:10.14278/rodare.557
url:https://rodare.hzdr.de/communities/ecfunded
url:https://rodare.hzdr.de/communities/health
url:https://rodare.hzdr.de/communities/rodare
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/4.0/legalcode
Preclinical
Image fusion
Proton radiation
Medical imaging
Histology
Slice2Volume: Fusion of multimodal medical imaging and light microscopy data of irradiation-injured brain tissue in 3D.
info:eu-repo/semantics/other
dataset
oai:rodare.hzdr.de:192
2021-12-15T14:06:56Z
software
user-health
user-rodare
Kornek, Dominik
Berthold, Jonathan
Kögler, Toni
2020-01-17
Single plane Compton imaging (SPCI) is a novel approach to medical imaging of gamma radiation [1]. The possible range of applications includes nuclear imaging and range verification in proton therapy. For the purpose of image reconstruction, a software tool written in ROOT [2] and named SPCI-Reconstruction [3] has been developed. The implementation features the well-established MLEM algorithm for binned data [4] as well as a Monte-Carlo based algorithm called Origin Ensemble [5]. Given a precalculated system matrix and a file containing the measurements, the emission densities of the gamma radiation source can be backprojected into a voxel-based image space.
[1] Pausch G et al. A novel scheme of compton imaging for nuclear medicine. 2016 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room-Temperature Semiconductor Detector Workshop (NSS/MIC/RTSD).
[2] CERN. ROOT – Data Analysis Framework. Release 6.12/04 - 2017-12-13. https://root.cern.ch/content/release-61204.
[3] Kornek D. Anwendung von Maximum-Likelihood Expectation-Maximization und Origin Ensemble zur Rekonstruktion von Aktivitätsverteilungen beim Single Plane Compton Imaging (SPCI). Master's thesis. TU Dresden. 2019.
[4] Shepp LA, Vardi Y. Maximum likelihood reconstruction for emission tomography. IEEE Trans Med Imaging. 1982; 1(2):113-22.
[5] Sitek A. Representation of photon limited data in emission tomography using origin ensembles. Phys Med Biol. 2008 June; 53(12):3201-3216.
https://rodare.hzdr.de/record/192
10.14278/rodare.192
oai:rodare.hzdr.de:192
eng
url:https://github.com/dkornek/SPCI-Reconstruction
url:https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-376408
url:https://gitlab.hzdr.de/fwmp/invivodos/SPCI_ReCo
url:https://www.hzdr.de/publications/Publ-30631
doi:10.14278/rodare.191
url:https://rodare.hzdr.de/communities/health
url:https://rodare.hzdr.de/communities/rodare
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/4.0/legalcode
single plane compton imaging
compton camera
image reconstruction
maximum-likelihood expectation-maximization
origin ensemble
nuclear medicine
range verification in particle therapy
SPCI-Reconstruction
info:eu-repo/semantics/other
software
oai:rodare.hzdr.de:2044
2022-12-22T10:41:13Z
openaire_data
user-health
user-rodare
Abdussalam, Wildan
2022-12-22
We provide post-processing data of daily infected COVID-19 cases for a municipality (Obec) level. The current data for municipality level is prepared on Czech_Obec_COVID19_Infections.csv. The file consists of five columns such as region, name, date, infected and population. The region denotes the ID of a county/state followed by its name in the next column. The inserted date of data is prepared in the third column followed by the number of dead and infected cases. Last but not least, the population of the county is provided in the last column.
This data hub was partially funded by the Where2Test project, which is financed by SMWK with tax funds on the basis of the budget approved by the Saxon State Parliament. This data hub was also partially funded by the Center of Advanced Systems Understanding (CASUS) which is financed by Germany’s Federal Ministry of Education and Research (BMBF) and by the Saxon Ministry for Science, Culture and Tourism (SMWK) with tax funds on the basis of the budget approved by the Saxon State Parliament.
https://rodare.hzdr.de/record/2044
10.14278/rodare.2044
oai:rodare.hzdr.de:2044
eng
url:https://www.hzdr.de/publications/Publ-35983
doi:10.14278/rodare.2043
url:https://rodare.hzdr.de/communities/health
url:https://rodare.hzdr.de/communities/rodare
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/4.0/legalcode
Covid-19
Czechia
Covid-19 Infections in Czechia
info:eu-repo/semantics/other
dataset
oai:rodare.hzdr.de:1811
2023-06-13T20:29:41Z
openaire_data
user-rodare
user-health
user-fwm
user-hzdr
Pausch, Guntram
Schellhammer, Sonja
Kögler, Toni
Berthold, Jonathan
Römer, Katja
Rinscheid, Andreas
Werner, Theresa
Hueso-González, Fernando
Kögler, Toni
Petzoldt, Johannes
Schellhammer, Sonja
Pausch, Guntram
2023-05-10
This dataset comprises the data reported on by Werner et al. (2019) in Phys. Med. Biol. 64 105023, 20pp (https://doi.org/10.1088/1361-6560/ab176d). Please refer to this publication for details on the experimental setup, data acquisition and preprocessing. The process is summarised in the following.
A static, pulsed pencil beam was delivered to a target without and with cylindrical air cavities of 5 to 20 mm thickness and prompt gamma-ray timing distributions were acquired.
Experimental setup:
A homogeneous cylindrical phantom comprised of poly(methylmethacrylate) was used. Air cavities of varying thickness ∆R ∈ {0 mm, 5 mm, 10 mm, 20 mm} were successively introduced into the phantom to mimic anatomical variations leading to range deviations. For each air cavity thickness, the phantom was irradiated with proton pencil beams of two different kinetic energies (E_1 = 162 MeV and E_2 = 227 MeV) and a micropulse repetition rate of 106.3 MHz. Prompt-gamma ray timing distributions were measured with a detection unit consisting of a single ∅2 ” × 2 ” CeBr_3 crystal by Scionix, a Hamamatsu R13089-100 photomultiplier and a U100 digital spectrometer by Target Systemelektronik, which was placed at a backward angle of 130° . A static pencil beam was directed centrally at the phantom. The beam was pulsed in spots with a spot duration of 69 ms, a period of 72 ms and 1e9 (!) protons per spot (corresponding approximately to the combined signal of 8 prompt-gamma ray detection units for one strongly weighted clinical pencil beam scanning spot). One measurement consisted of 100 spots. Overall, the experiment comprised eight measurements covering the set of four cavity thicknesses ∆R and two beam energies E_1 and E_2. Experiments were carried out in the patient treatment room of OncoRay, Dresden.
Data preprocessing:
The raw data of each measurement was preprocessed as follows: The binary data was converted to ROOT. The photomultiplier gain drift was corrected for and the integral signal charge was converted into deposited energy. Time digitalisation nonlinearities were corrected for. The calibrated data was then saved in list-mode format. The data was assigned to the spot number and the detection time relative to the accelerator radiofrequency (fine time) was used to populate a prompt gamma-ray timing histogram for each spot. No background or phase shift correction were applied.
Data structure:
The dataset contains one root file for each measurement, named by the detector number in the format u100-p00XX and the measurement time. The spreadsheet MeasurementIndex_20160716_SingleSpot.xlsx contains the details of each measurement. The corrected and calibrated PGT spectra can be found in the root file at analysis/05_PGT_for_Layers_and_Spots.
Each root file contains the following directories:
analysis
01_Layers_and_Spots_Detection: association between spot number and measurement time
02_Gain_Correction: energy gain drift correction curve
03_Energy_Calibration: energy calibration curve
04_Fine_Time_Linearization: timing non-linearity calibration curve
05_PGT_for_Layers_and_Spots: final PGT spectra - for each spot of each layer:
PGT_*_all: timing spectrum of the whole energy range
PGT_*_2,5to7MeV: timing spectrum for events between 2.5 and 7 MeV only
PGT_*_3to5MeV: timing spectrum for events between 3 and 5 MeV only
ESpec: energy spectrum
EoT: two-dimensional energy-timing spectrum
data: list-mode data (not histogrammed)
uncorrected: before the correction and calibration steps
corrected: after the correction and calibration steps
meta: measurement meta data (log file containing applied detector HV etc.)
histograms: selected example histograms
For further questions, please refer to the contact persons stated in the Contributors section.
https://rodare.hzdr.de/record/1811
10.14278/rodare.1811
oai:rodare.hzdr.de:1811
eng
url:https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-769231
url:https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-840468
doi:10.1088/1361-6560/ab176d
doi:10.3389/fphy.2022.932950
url:https://www.hzdr.de/publications/Publ-36784
doi:10.14278/rodare.1810
url:https://rodare.hzdr.de/communities/fwm
url:https://rodare.hzdr.de/communities/health
url:https://rodare.hzdr.de/communities/hzdr
url:https://rodare.hzdr.de/communities/rodare
info:eu-repo/semantics/restrictedAccess
proton therapy
treatment verification
prompt gamma-ray timing
experimental data
Experimental prompt gamma-ray timing data for proton treatment verification in a clinical facility using a fixed beam
info:eu-repo/semantics/other
dataset
oai:rodare.hzdr.de:1946
2022-11-10T14:16:18Z
software
user-health
user-hzdr
user-rodare
Abdussalam, Wildan
2022-11-10
Software to synchonise the data between various data sources and casus database server. For Unix users please use MigrateWhere2test_2.0Unix.zip and for WIndows users please use MigrateWhere2test_2.0Win.zip. In order to use the scripts, please use the following instructions:
Windows
1. Create the postgreq sql database and set the port 5432.
2. Apply w2testRegion.sql to the Postgresql database. It creates the schema of the database.
3. Create folder C:\Workspaces and unzip the unix file.
4. Create folder in workspaces, com.com.casus.env.where2test.migration\COM_CASUS_WHERE2TEST_MIGRATION and then unzip the source file inside COM_CASUS_WHERE2TEST_MIGRATION.
5. Install requirement.txt. We use some python scripts for downloading and cleaning the data
6. Set the memory on the file MigrateWhere2test_run.bat minimal to 13 GB
7. Set run Develop and run the .bat file on the folder MigrateWhere2test_2.0Unix to run in localhost.
Unix
1. Create PostgreSQL with port 32771.
2. Apply w2testRegion.sql to the Postgresql database. It creates the schema of the database.
3. Create folder /home/wildan/Workspaces and unzip the unix file.
4. Open the file MigrateWhere2test/MigrateWhere2test_run.sh and change the mode "Default" by "Production"
5. Create folder in workspaces, com.com.casus.env.where2test.migration.unix/COM_CASUS_WHERE2TEST_MIGRATION and then unzip the source file inside COM_CASUS_WHERE2TEST_MIGRATION.
6. Install requirement.txt. We use some python scripts for downloading and cleaning the data
7. Set the memory on the file MigrateWhere2test_run.sh minimal to 13 GB
8. run the MigrateWhere2test_run.sh in the "Production" mode.
https://rodare.hzdr.de/record/1946
10.14278/rodare.1946
oai:rodare.hzdr.de:1946
eng
url:https://www.hzdr.de/publications/Publ-34430
doi:10.14278/rodare.1500
url:https://rodare.hzdr.de/communities/health
url:https://rodare.hzdr.de/communities/hzdr
url:https://rodare.hzdr.de/communities/rodare
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/4.0/legalcode
data pipeline
Data synchronizator of Where2test pipeline
info:eu-repo/semantics/other
software
oai:rodare.hzdr.de:116
2019-03-06T14:02:35Z
openaire_data
user-ecfunded
user-health
user-hzdr
user-matter
user-rodare
Huebl, Axel
Rehwald, Martin
Obst-Huebl, Lieselotte
Ziegler, Tim
Garten, Marco
Widera, René
Zeil, Karl
Cowan, Thomas E.
Bussmann, Michael
Schramm, Ulrich
Kluge, Thomas
2019-03-06
Supplementary materials for our paper "Spectral Control via Multi-Species Effects in PW-Class Laser-Ion Acceleration".
Additional high-resolution, raw HDF5 files using the openPMD standard (DOI:10.5281/zenodo.1167843) increase simulation output data to 4.7 TByte and are available from the corresponding author upon reasonable request.
This project received funding within the MEPHISTO project (BMBF-Förderkennzeichen 01IH16006C).
https://rodare.hzdr.de/record/116
10.14278/rodare.116
oai:rodare.hzdr.de:116
eng
info:eu-repo/grantAgreement/EC/H2020/654148/
url:https://www.hzdr.de/publications/Publ-28969
doi:10.14278/rodare.115
url:https://rodare.hzdr.de/communities/ecfunded
url:https://rodare.hzdr.de/communities/health
url:https://rodare.hzdr.de/communities/hzdr
url:https://rodare.hzdr.de/communities/matter
url:https://rodare.hzdr.de/communities/rodare
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-sa/4.0/legalcode
LPA
laser-ion acceleration
TNSA
multi-species
cryogenic target
particle-in-cell
Supplementary Data: Spectral Control via Multi-Species Effects in PW-Class Laser-Ion Acceleration
info:eu-repo/semantics/other
dataset
oai:rodare.hzdr.de:2285
2023-05-22T07:24:33Z
user-rodare
user-health
Podlipec, Rok
2023-05-03
Time-lapse videos of cells and microtubule dynamics (in green) after exposure to different nanoparticles (in red) taken with confocal fluorescence microscopy. S1 - control experiment; S2-S3 - the exposure to titanium dioxide (TiO2) nanotubes measured at different image planes; S4-S5 - the exposure to TiO2 nanocubes measured at different image planes; S6-S7 - the exposure to multiwall carbon nanotubes (MWCNTs) measured at different image planes.
https://rodare.hzdr.de/record/2285
10.14278/rodare.2285
oai:rodare.hzdr.de:2285
doi:10.14278/rodare.2287
url:https://www.hzdr.de/publications/Publ-36911
url:https://www.hzdr.de/publications/Publ-36910
doi:10.14278/rodare.2284
url:https://rodare.hzdr.de/communities/health
url:https://rodare.hzdr.de/communities/rodare
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/4.0/legalcode
Different effect of anatase TiO2 nanotubes and nanocubes on microtubule fragmentation, mitotic arrest and aneuploidy indicating plausible carcinogenicity
info:eu-repo/semantics/other
video
oai:rodare.hzdr.de:1261
2021-12-15T14:31:24Z
openaire_data
user-health
user-rodare
Moldovan, Rares-Petru
2021-11-11
Cannabinoid receptors type 2 (CB2R) represent an attractive therapeutic target for neurodegenerative diseases and cancer. Aiming at a positron emission tomography (PET) radiotracer to monitor receptor density and/or occupancy during a CB2R-tailored therapy, we developed here cis-[18F]1-(4-fluorobutyl-N-((1s,4s)-4-methylcyclohexyl)-2-oxo-1,2-dihydro-1,8-naphthyridine-3-carboxamide ([18F]LU14) starting from the corresponding mesylate precursor. First biological evaluation revealed that [18F]LU14 is a highly affine CB2R radioligand with >80% intact tracer in brain at 30 min p.i. Its further evaluation in a well-established rat model of CB2R overexpression by PET demonstrated its ability to selectively image the CB2R in the brain and its potential as tracer to further investigate diseased related CB2R changes in expression.
https://rodare.hzdr.de/record/1261
10.14278/rodare.1261
oai:rodare.hzdr.de:1261
url:https://www.hzdr.de/publications/Publ-33389
url:https://www.hzdr.de/publications/Publ-32557
doi:10.14278/rodare.1260
url:https://rodare.hzdr.de/communities/health
url:https://rodare.hzdr.de/communities/rodare
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/4.0/legalcode
Cannabinoid receptor type 2
naphtyrid-2-one
binding affinity
radiochemistry
fluorine-18 labeling
brain
positron emission tomography
Data Publication: Development of [18F]LU14 for PET Imaging of Cannabinoid Receptor Type 2 in the Brain
info:eu-repo/semantics/other
dataset
oai:rodare.hzdr.de:2562
2024-01-29T14:42:42Z
openaire_data
user-health
user-rodare
Liou, Natasha
De, Trina
Urbanski, Adrian
Khasriya, Rajvinder
Yakimovich, Artur
Horsley, Harry
2023-09-12
Urinary tract infections (UTI) are a common disorder. Its diagnosis can be made by microscopic examination of voided urine for cellular markers of infection. We present a dataset containing 300 images and 3,562 manually annotated urinary cells labelled into seven classes of clinically significant urinary content. It is an enriched dataset with samples acquired from the unstained and untreated urine of patients with symptomatic UTI. The aim of the dataset is to facilitate UTI diagnosis in nearly all clinical settings by using a simple imaging system which leverages advanced machine learning techniques.
Data acquisition
300 urine samples were obtained from patients with symptomatic UTI between April and August 2022 from a specialist LUTS outpatient clinic in central London. Urine samples were collected as natural voids and processed on-site within one hour to mitigate cellular degradation. Brightfield microscopic examination (Olympus BX41F microscope frame, U-5RE quintuple nosepiece, U-LS30 LED illuminator, U-AC Abbe condenser) was performed at x20 objective (Olympus PLCN20x Plan C N Achromat 20x/0.4). A disposable haemocytometer (C Chip™) was used for enumeration of red cells (RBC), white cells (WBC), epithelial cells (EPC), and the presence of other cellular content per 1 µl of urine by two experienced microscopists.
Images were acquired using the aforementioned brightfield microscope using a 0.5X C-mount adapter connected to a digital colour camera (Infinity 3S-1UR, Teledyne Lumenera). Images were taken in 16-bit colour in 1392 x 1040 .tif format using Capture and Analyse software. An enriched dataset approach was taken to maximise urinary cellular content in the acquired images. Such data curation was also necessary to overcome class imbalance. Daily Kohler illumination and global white balance was performed to ensure consistency in image acquisition.
Dataset annotation
300 images were acquired and manually annotated by first identifying cells of interest as a binary semantic segmentation task. Individual pixels were dichotomously labelled as either informative cells, foreground, or non-informative background. Non-informative background was further constrained by including unidentifiable cells, such as debris or grossly out-of-focus particles. Binary annotation was initially performed using ilastik, an open-source software using a Random Forest classifier for pixel classification, then manually refined at the pixel level to ensure accurate semantic segmentation. This produced a binary mask in 1392 x 1040 .tif format for each corresponding raw colour image.
Objects of interest were then manually labelled by two expert microscopists into one of seven clinically significant multi-class categories: rods, RBC/WBC, yeast, miscellaneous, single EPC, small EPC sheet, and large EPC sheet. This produced a multi-class mask in 1392 x 1040 .tif format with a label as pixel value from 0-7, where 0 is background (Table 1).
Data structure
The dataset is organised into three root folders: img (image), bin_mask (binary mask), and mult_mask (multi-class mask). Each folder has 300 files in .tif format and labelled with an incremental number.
Table1
Folder Files Objects Count Pixel Values
img 300 Raw data 0-255
bin_mask 300 Background/Foreground 0/1
mult_mask 300 Background/Class 0
Rod 1697 1
RBC/WBC 1056 2
Yeast 41 3
Miscellaneous 550 4
Single EPC 182 5
Small EPC sheet 26 6
Large EPC sheet 10 7
Total 3562
https://rodare.hzdr.de/record/2562
10.14278/rodare.2562
oai:rodare.hzdr.de:2562
eng
url:https://www.hzdr.de/publications/Publ-37531
doi:10.14278/rodare.2472
url:https://rodare.hzdr.de/communities/health
url:https://rodare.hzdr.de/communities/rodare
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/4.0/legalcode
clinical microscopy
urine microscopy
widefield
transmission light
image segmentation
binary segmentation
multiclass segmentation
Clinical urine microscopy for urinary tract infections
info:eu-repo/semantics/other
dataset
oai:rodare.hzdr.de:2563
2024-01-29T14:42:42Z
openaire_data
user-health
user-rodare
Liou, Natasha
De, Trina
Urbanski, Adrian
Khasriya, Rajvinder
Yakimovich, Artur
Horsley, Harry
2023-09-12
Urinary tract infections (UTI) are a common disorder. Its diagnosis can be made by microscopic examination of voided urine for cellular markers of infection. We present a dataset containing 300 images and 3,562 manually annotated urinary cells labelled into seven classes of clinically significant urinary content. It is an enriched dataset with samples acquired from the unstained and untreated urine of patients with symptomatic UTI. The aim of the dataset is to facilitate UTI diagnosis in nearly all clinical settings by using a simple imaging system which leverages advanced machine learning techniques.
Data acquisition
300 urine samples were obtained from patients with symptomatic UTI between April and August 2022 from a specialist LUTS outpatient clinic in central London. Urine samples were collected as natural voids and processed on-site within one hour to mitigate cellular degradation. Brightfield microscopic examination (Olympus BX41F microscope frame, U-5RE quintuple nosepiece, U-LS30 LED illuminator, U-AC Abbe condenser) was performed at x20 objective (Olympus PLCN20x Plan C N Achromat 20x/0.4). A disposable haemocytometer (C Chip™) was used for enumeration of red cells (RBC), white cells (WBC), epithelial cells (EPC), and the presence of other cellular content per 1 µl of urine by two experienced microscopists.
Images were acquired using the aforementioned brightfield microscope using a 0.5X C-mount adapter connected to a digital colour camera (Infinity 3S-1UR, Teledyne Lumenera). Images were taken in 16-bit colour in 1392 x 1040 .tif format using Capture and Analyse software. An enriched dataset approach was taken to maximise urinary cellular content in the acquired images. Such data curation was also necessary to overcome class imbalance. Daily Kohler illumination and global white balance was performed to ensure consistency in image acquisition.
Dataset annotation
300 images were acquired and manually annotated by first identifying cells of interest as a binary semantic segmentation task. Individual pixels were dichotomously labelled as either informative cells, foreground, or non-informative background. Non-informative background was further constrained by including unidentifiable cells, such as debris or grossly out-of-focus particles. Binary annotation was initially performed using ilastik, an open-source software using a Random Forest classifier for pixel classification, then manually refined at the pixel level to ensure accurate semantic segmentation. This produced a binary mask in 1392 x 1040 .tif format for each corresponding raw colour image.
Objects of interest were then manually labelled by two expert microscopists into one of seven clinically significant multi-class categories: rods, RBC/WBC, yeast, miscellaneous, single EPC, small EPC sheet, and large EPC sheet. This produced a multi-class mask in 1392 x 1040 .tif format with a label as pixel value from 0-7, where 0 is background (Table 1).
Data structure
The dataset is organised into three root folders: img (image), bin_mask (binary mask), and mult_mask (multi-class mask). Each folder has 300 files in .tif format and labelled with an incremental number.
Table1
Folder Files Objects Count Pixel Values
img 300 Raw data 0-255
bin_mask 300 Background/Foreground 0/1
mult_mask 300 Background/Class 0
Rod 1697 1
RBC/WBC 1056 2
Yeast 41 3
Miscellaneous 550 4
Single EPC 182 5
Small EPC sheet 26 6
Large EPC sheet 10 7
Total 3562
https://rodare.hzdr.de/record/2563
10.14278/rodare.2563
oai:rodare.hzdr.de:2563
eng
url:https://www.hzdr.de/publications/Publ-37531
doi:10.14278/rodare.2472
url:https://rodare.hzdr.de/communities/health
url:https://rodare.hzdr.de/communities/rodare
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/4.0/legalcode
clinical microscopy
urine microscopy
widefield
transmission light
image segmentation
binary segmentation
multiclass segmentation
Clinical urine microscopy for urinary tract infections
info:eu-repo/semantics/other
dataset
oai:rodare.hzdr.de:1436
2023-08-17T11:05:16Z
openaire_data
user-health
user-rodare
Sharma, Vaibhav
Yakimovich, Artur
2023-01-16
Sample preparation artefacts represent a significant source of errors in high-content screening datasets leading to misinterpretation of results in drug discovery. To address this we have created a multispectral high-content imaging dataset with typical sample preparation artefacts added to the samples. This dataset consists of high-content images of cultured HeLa ATCC cells in the presence of typical sample preparation artefacts. The aim of this dataset. HeLa cells imaged in this dataset were cultured in a black 96-well (rows A to H and columns 1 to 12) polystyrene imaging plate (Corning, Sigma).
To obtain a dataset similar to the experimental setup of a high-content image-based screening we have used a 96-well (rows A to H and columns 1 to 12) black polystyrene imaging plate (Corning, Sigma). HeLa cells were seeded a day prior to the experiment in 200 µL volume (per well) containing 250000 cells per mL in Dulbecco’s Modified Eagle’s Medium (Sigma) containing 10% fetal calf serum (Sigma) 4500 mg/L glucose (Sigma), sodium bicarbonate (Sigma), L-glutamine (Sigma), sodium pyruvate (Sigma), and non-essential amino acids (Sigma). To obtain a gradient of cell density, the cell suspension was stepwise diluted at 1:2 ratio during seeding (columns 2 to 12). The first column was reserved as no-cells control. Upon seeding, the HeLa cells were incubated overnight at 37° C in a 5% CO2 atmosphere with humidity control. The next day after seeding, cells were fixed with 4% paraformaldehyde (Sigma) solution prepared in phosphate buffer saline (PBS, Sigma). Upon fixation, HeLa cell nuclei were stained with Hoechst 33342 dye (Sigma) at 40 µg/mL concentration prepared in PBS. Row A was kept unstained as the control without Hoechst dye. Upon preparation of the bona fide artefact-free experimental plate, we have collected samples of dust across the approximately 100 m2 laboratory and prepared a suspension of these dust samples in PBS. Next, we added this suspension to rows A to G of the 96-well plate, leaving row H as an artefact-free control.
The dataset consists of images obtained with 4x and 10x objectives using fluorescence cube assemblies for DAPI, CFP, GFP, TRITC and Cy5 channels. For hardware reasons, images with the CFP filter cube were obtained separately from images with DAPI, CFP, GFP, TRITC and Cy5 filter cubes. Furthermore, CFP images (and in some cases DAPI images) were obtained with varying exposure times corresponding to “_w1”, “_w2” and so on filename suffixes. Images were obtained using ImageXpress Micro XL high-content microscope (Molecular Devices). Images are organised into the following folders:
4x-cfp
4x-dapi-gfp-tritc-cy5
10x-6cfp
10x-6dapi
10x-cfp
dapi-gfp-tritc-cy5
filters_spectra
Here, folders A and B correspond to 4x magnification and contain images obtained with the CFP (folder A) and the other filter cubes respectively (folder B). Each folder contains “TimePoint_1” subfolder containing the raw images. In the case of 4x images, each field of view (“site” designed with “_s1”, “_s2” etc. suffixes) corresponds to a nearly perfect quarter of a 96-well plate well. In addition to the raw images in the “TimePoint_1”, a subfolder “Stitched” contains images of the entire wells. In the case of folder B containing all other fluorescence channels, “_w1”, “_w2”, “_w3”, and “_w4” correspond to a single optimal exposure time of DAPI, GFP, TRITC and Cy5 filters respectively.
Similarly, folders C - F correspond to 10x magnification and contain images of multiple exposures of CFP and DAPI (folders C and D) and single exposures of CFP and other channels (folders E and F). In the case of CFP and DAPI multiple exposures folders, varying exposure times correspond to “_w1”, “_w2” etc. Finally, folder G contains metadata on filter cubes used in the dataset, including the emission and excitation filters spectra for each filter cube.
https://rodare.hzdr.de/record/1436
10.14278/rodare.1436
oai:rodare.hzdr.de:1436
url:https://www.hzdr.de/publications/Publ-36282
doi:10.14278/rodare.1435
url:https://rodare.hzdr.de/communities/health
url:https://rodare.hzdr.de/communities/rodare
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/4.0/legalcode
fluorescence microscopy
high-content microscopy
sample preparation artefacts
High-content multi-spectral fluorescence microscopy sample preparation artefacts
info:eu-repo/semantics/other
dataset
oai:rodare.hzdr.de:2262
2023-05-03T09:51:47Z
openaire_data
user-ecfunded
user-rodare
user-health
user-matter
Reimold, Marvin
Assenbaum, Stefan
Beyreuther, Elke
Bodenstein, Elisabeth
Brack, Florian-Emanuel
Eisenmann, Christoph
Englbrecht, F.
Kroll, Florian
Lindner, F.
Masood, U.
Pawelke, Jörg
Schramm, Ulrich
Schneider, Moritz
Sobiella, Manfred
Umlandt, Marvin Elias Paul
Vescovi Pinochet, Milenko Andrés
Zeil, Karl
Ziegler, Tim
Metzkes-Ng, Josefine
2023-04-18
The data set comprises raw data, processed data and detector data for the OCTOPOD detector applied for characterization of proton bunches at a proton cyclotron and a laser-driven proton accelerator.
https://rodare.hzdr.de/record/2262
10.14278/rodare.2262
oai:rodare.hzdr.de:2262
eng
info:eu-repo/grantAgreement/EC/H2020/871124/
url:https://www.hzdr.de/publications/Publ-36823
url:https://www.hzdr.de/publications/Publ-36751
doi:10.14278/rodare.2261
url:https://rodare.hzdr.de/communities/ecfunded
url:https://rodare.hzdr.de/communities/health
url:https://rodare.hzdr.de/communities/matter
url:https://rodare.hzdr.de/communities/rodare
info:eu-repo/semantics/restrictedAccess
laser-plasma acceleration of protons
proton detector
tomographic reconstruction
Data publication for: OCTOPOD - single bunch tomography for angular-spectral characterization of laser-driven protons
info:eu-repo/semantics/other
dataset
oai:rodare.hzdr.de:915
2023-05-30T13:11:26Z
openaire_data
user-ecfunded
user-health
user-rodare
Müller, Johannes
Suckert, Theresa
Beyreuther, Elke
Schneider, Moritz
Boucsein, Marc
Bodenstein, Elisabeth
Stolz-Kieslich, Liane
Krause, Mechthild
von Neubeck, Cläre
Haase, Robert
Lühr, Armin
Dietrich, Antje
2021-01-20
The dataset contains comprehensive image data for a total of nine mice, which underwent normal tissue brain irradiation with 90 MeV protons.
In particular, the image data comprise cone-bem computed tomographies (CBCT), Monte Carlo beam transport simulations based on those CTs, regular magnetic resonance imaging (MRI) follow-up (≥ 26 weeks), a co-aligned DSURQE mouse brain atlas and scanned whole-brain tissue sections with histochemical and immunofluorescent markers for morphology (H&E), cell nuclei (DAPI), astrocytes (GFAP), microglia (Iba1), the intermediate filament protein Nestin, proliferation (Ki67), neurons (NeuN) and oligodendrocytes (OSP).
The volumetric image data (i.e. CBCT, MRI and brain atlas) were co-aligned using the ImageJ plugin Big Warp. The CBCT data was used as spatial reference to allow for mask-based, slice-wise alignment of CBCT and light microscopy image data in 3D with the scriptable registration tool Elastix.
We provide the data in raw format and as aligned data sets, as well as their spatial transformations.
Chunked zip: The histological data are stored as chunked .zip files (*.zip.001 - *.zip.0XX). In order to unpack the data, download all chunks into the same directory, then unpack.
https://rodare.hzdr.de/record/915
10.14278/rodare.915
oai:rodare.hzdr.de:915
eng
info:eu-repo/grantAgreement/EC/H2020/730983/
url:https://www.hzdr.de/publications/Publ-32124
url:https://www.hzdr.de/publications/Publ-31469
doi:10.14278/rodare.557
url:https://rodare.hzdr.de/communities/ecfunded
url:https://rodare.hzdr.de/communities/health
url:https://rodare.hzdr.de/communities/rodare
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/4.0/legalcode
Preclinical
Image fusion
Proton radiation
Medical imaging
Histology
Slice2Volume: Fusion of multimodal medical imaging and light microscopy data of irradiation-injured brain tissue in 3D.
info:eu-repo/semantics/other
dataset
oai:rodare.hzdr.de:2442
2023-08-17T11:46:11Z
openaire_data
user-health
user-rodare
Sharma, Vaibhav
Yakimovich, Artur
2023-01-16
Sample preparation artefacts represent a significant source of errors in high-content screening datasets leading to misinterpretation of results in drug discovery. To address this we have created a multispectral high-content imaging dataset with typical sample preparation artefacts added to the samples. This dataset consists of high-content images of cultured HeLa ATCC cells in the presence of typical sample preparation artefacts. The aim of this dataset. HeLa cells imaged in this dataset were cultured in a black 96-well (rows A to H and columns 1 to 12) polystyrene imaging plate (Corning, Sigma).
To obtain a dataset similar to the experimental setup of a high-content image-based screening we have used a 96-well (rows A to H and columns 1 to 12) black polystyrene imaging plate (Corning, Sigma). HeLa cells were seeded a day prior to the experiment in 200 µL volume (per well) containing 250000 cells per mL in Dulbecco’s Modified Eagle’s Medium (Sigma) containing 10% fetal calf serum (Sigma) 4500 mg/L glucose (Sigma), sodium bicarbonate (Sigma), L-glutamine (Sigma), sodium pyruvate (Sigma), and non-essential amino acids (Sigma). To obtain a gradient of cell density, the cell suspension was stepwise diluted at 1:2 ratio during seeding (columns 2 to 12). The first column was reserved as no-cells control. Upon seeding, the HeLa cells were incubated overnight at 37° C in a 5% CO2 atmosphere with humidity control. The next day after seeding, cells were fixed with 4% paraformaldehyde (Sigma) solution prepared in phosphate buffer saline (PBS, Sigma). Upon fixation, HeLa cell nuclei were stained with Hoechst 33342 dye (Sigma) at 40 µg/mL concentration prepared in PBS. Row A was kept unstained as the control without Hoechst dye. Upon preparation of the bona fide artefact-free experimental plate, we have collected samples of dust across the approximately 100 m2 laboratory and prepared a suspension of these dust samples in PBS. Next, we added this suspension to rows A to G of the 96-well plate, leaving row H as an artefact-free control.
The dataset consists of images obtained with 4x and 10x objectives using fluorescence cube assemblies for DAPI, CFP, GFP, TRITC and Cy5 channels. For hardware reasons, images with the CFP filter cube were obtained separately from images with DAPI, CFP, GFP, TRITC and Cy5 filter cubes. Furthermore, CFP images (and in some cases DAPI images) were obtained with varying exposure times corresponding to “_w1”, “_w2” and so on filename suffixes. Images were obtained using ImageXpress Micro XL high-content microscope (Molecular Devices). Images are organised into the following folders:
4x-cfp
4x-dapi-gfp-tritc-cy5
10x-6cfp
10x-6dapi
10x-cfp
dapi-gfp-tritc-cy5
filters_spectra
Here, folders A and B correspond to 4x magnification and contain images obtained with the CFP (folder A) and the other filter cubes respectively (folder B). Each folder contains “TimePoint_1” subfolder containing the raw images. In the case of 4x images, each field of view (“site” designed with “_s1”, “_s2” etc. suffixes) corresponds to a nearly perfect quarter of a 96-well plate well. In addition to the raw images in the “TimePoint_1”, a subfolder “Stitched” contains images of the entire wells. In the case of folder B containing all other fluorescence channels, “_w1”, “_w2”, “_w3”, and “_w4” correspond to a single optimal exposure time of DAPI, GFP, TRITC and Cy5 filters respectively.
Similarly, folders C - F correspond to 10x magnification and contain images of multiple exposures of CFP and DAPI (folders C and D) and single exposures of CFP and other channels (folders E and F). In the case of CFP and DAPI multiple exposures folders, varying exposure times correspond to “_w1”, “_w2” etc. Finally, folder G contains metadata on filter cubes used in the dataset, including the emission and excitation filters spectra for each filter cube.
https://rodare.hzdr.de/record/2442
10.14278/rodare.2442
oai:rodare.hzdr.de:2442
url:https://www.hzdr.de/publications/Publ-36282
doi:10.14278/rodare.1435
url:https://rodare.hzdr.de/communities/health
url:https://rodare.hzdr.de/communities/rodare
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/4.0/legalcode
fluorescence microscopy
high-content microscopy
sample preparation artefacts
High-content multi-spectral fluorescence microscopy sample preparation artefacts
info:eu-repo/semantics/other
dataset
oai:rodare.hzdr.de:66
2018-10-30T12:42:21Z
openaire_data
user-fwk
user-health
user-hzdr
user-matter
user-rodare
Obst-Huebl, Lieselotte
Ziegler, Tim
Brack, Florian-Emanuel
Branco, João
Bussmann, Michael
Cowan, Thomas E.
Curry, Chandra B.
Fiuza, Frederico
Garten, Marco
Gauthier, Maxence
Göde, Sebastian
Glenzer, Siegfried H.
Huebl, Axel
Irman, Arie
Kim, Jongjin B.
Kluge, Thomas
Kraft, Stephan
Kroll, Florian
Metzkes-Ng, Josefine
Pausch, Richard
Prencipe, Irene
Rehwald, Martin
Rödel, Christian
Schlenvoigt, Hans-Peter
Schramm, Ulrich
Zeil, Karl
2018-10-30
This data repository contains analyzed data files of the shown figures and simulation input files.
Please see the according README.txt files in the individual directories and the original manuscript for guidance.
Manuscript title:
All-optical structuring of laser-driven proton beam profiles
Authors:
Lieselotte Obst, Tim Ziegler, Florian-Emanuel Brack, Joao Branco, Michael Bussmann, Thomas E. Cowan, Chandra B. Curry, Frederico Fiuza, Marco Garten, Maxence Gauthier, Sebastian Göde, Siegfried H. Glenzer, Axel Huebl, Arie Irman, Siegfried H. Glenzer, Axel Huebl, Arie Irman, Jongjin B. Kim, Thomas Kluge, Stephan Kraft, Florian Kroll, Josefine Metzkes-Ng, Richard Pausch, Irene Prencipe, Martin Rehwald, Christian Rödel, Hans-Peter Schlenvoigt, Ulrich Schramm, Karl Zeil
Submitted to:
Nature Communications (2018)
Responsible for the data repository:
Lieselotte Obst-Huebl, TU Dresden and HZDR
Axel Huebl, TU Dresden and HZDR
Tim Ziegler, TU Dresden and HZDR
Thomas Kluge, HZDR
https://rodare.hzdr.de/record/66
10.14278/rodare.66
oai:rodare.hzdr.de:66
url:https://www.hzdr.de/publications/Publ-28136
doi:10.14278/rodare.65
url:https://rodare.hzdr.de/communities/fwk
url:https://rodare.hzdr.de/communities/health
url:https://rodare.hzdr.de/communities/hzdr
url:https://rodare.hzdr.de/communities/matter
url:https://rodare.hzdr.de/communities/rodare
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/4.0/legalcode
All-optical structuring of laser-driven proton beam profiles data sets
info:eu-repo/semantics/other
dataset
oai:rodare.hzdr.de:255
2020-10-30T13:02:40Z
openaire_data
user-health
user-rodare
Starke, Sebastian
Leger, Stefan
Zwanenburg, Alex
Leger, Karoline
Lohaus, Fabian
Linge, Annett
Schreiber, Andreas
Kalinauskaite, Goda
Tinhofer, Inge
Guberina, Nika
Guberina, Maja
Balermpas, Panagiotis
Grün, Jens von der
Ganswindt, Ute
Belka, Claus
Peeken, Jan C.
Combs, Stephanie E.
Böke, Simon
Zips, Daniel
Richter, Christian
Troost, Esther Gera Cornelia
Krause, Mechthild
Baumann, Michael
Löck, Steffen
2020-02-27
These are the results from the analyses presented in a paper submitted to Scientific Reports.
The zip file contains the trained model files and the plots that were used in the manuscript.
Code for reproduction of our analyses can be obtained from https://github.com/oncoray/cnn-hnscc. There, you also find instructions on how to load our models.
https://rodare.hzdr.de/record/255
10.14278/rodare.255
oai:rodare.hzdr.de:255
url:https://www.hzdr.de/publications/Publ-30759
url:https://www.hzdr.de/publications/Publ-30750
doi:10.14278/rodare.254
url:https://rodare.hzdr.de/communities/health
url:https://rodare.hzdr.de/communities/rodare
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc/4.0/legalcode
convolutional neural networks
Keras
Deep learning
head and neck cancer
loco-regional-recurrence
Cox proportional hazards
2D and 3D convolutional neural networks for outcome modelling of locally advanced head and neck squamous cell carcinoma
info:eu-repo/semantics/other
dataset
oai:rodare.hzdr.de:2668
2024-01-17T08:38:48Z
openaire_data
user-rodare
user-health
Li, Rui
Della Maggiora Valdes, Gabriel Eugenio
Andriasyan, Vardan
Petkidis, Anthony
Yushkevich, Artsemi
Kudryashev, Mikhail
Yakimovich, Artur
2024-01-12
Dataset
This dataset contains a sample of 600 fluorescently labelled nuclei of cultured cells imaged using widefield fluorescence microscopy and confocal fluorescence microscopy at different focal planes.
Image preprocessing
Notably, the hardware precision of the sectioning process led to variations in the step size when shifting the focal plane between the two devices. This resulted in distinct z-dimensions between the datasets obtained from the two microscopy techniques. The confocal stacks in raw data comprised 92 focal planes, whereas the widefield stacks consisted of only 40 slices. Each focal plane image had a shape [2048, 2048, 1]. Assuming the central slice of each stack to be the in-focus, we performed z-direction registration by downsampling the confocal stacks from the central slice (46th) to match the 40 slices of the widefield stacks. Due to the instrumental limitations, a slight drift was noticeable between images. To address this, we used the phase cross-correlation algorithm [2] to compensate for the offsets on the x-y plane for the z-dimension registered image stacks. Having completed the registration and alignment along three dimensions, we then partitioned the original images into non-overlapping patches with dimensions of [128, 128, 1] in the xy plane. This partitioned dataset serves as the test dataset for validating our blind-deconvolution model, conducted without the specific Point Spread Function (PSF) parameters [3].
Files description
The Widefield-confocal Microscopy Dataset is stored in the '*.npz' format, encompassing the variables 'c_img' and 'w_img.' These handles respectively denote the confocal images and their corresponding widefield microscopy images. Both types of data undergo registration, alignment, and normalization, with values scaled to range between [0.0, 1.0]. For each category, the data has a shape of [600, 128, 128, 40], where the first dimension denotes the individual field of view and the last dimension signifies the z-dimension representing changes in the focal plane for virtual sectioning. The first dimension corresponds to the patch number, each with a patch size of [128, 128].
Sample preparation and microscopy
A549 lung carcinoma cell line cells were seeded in 96-well imaging plates a night prior to imaging, then fixed with 4% paraformaldehyde (Sigma) and stained for DNA with Hoechst 33342 fluorescent dye (Sigma). Cell culture was maintained similarly to the procedures described in [1]. Next, stained cell nuclei were imaged using ImageXpress Confocal system (Molecular Devices) in either confocal or widefield mode employing Nikon 20X Plan Apo Lambda objective. To obtain 3D information images in both modes were acquired as Z-stacks with 0.3 µm and 0.7 µm for confocal and widefield modes respectively. Confocal z-stack was Nyquist sampled. The excitation wavelength was 405 nm and the emission was 452 nm. Using these settings, we obtained individual stacks for both modalities, with each stack covering 2048 by 2048 pixels or 699 by 699 µm.
References
Yakimovich, Artur, et al. "Plaque2. 0—a high-throughput analysis framework to score virus-cell transmission and clonal cell expansion." PloS one 10.9 (2015): e0138760.
Alink, Mark S. Oude, et al. "Lowering the SNR wall for energy detection using cross-correlation." IEEE transactions on vehicular technology 60.8 (2011): 3748-3757.
Li, Rui, et al. "Microscopy image reconstruction with physics-informed denoising diffusion probabilistic model." arXiv preprint arXiv:2306.02929 (2023).
https://rodare.hzdr.de/record/2668
10.14278/rodare.2668
oai:rodare.hzdr.de:2668
eng
arxiv:2306.02929
url:https://www.hzdr.de/publications/Publ-37066
doi:10.48550/arXiv.2306.02929
url:https://www.hzdr.de/publications/Publ-38497
url:https://www.hzdr.de/publications/Publ-37066
doi:10.14278/rodare.2667
url:https://rodare.hzdr.de/communities/health
url:https://rodare.hzdr.de/communities/rodare
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/4.0/legalcode
fluorescence microscopy
widefield
confocal
corelative microscopy
Correlated Widefield-confocal Microscopy Dataset
info:eu-repo/semantics/other
dataset
oai:rodare.hzdr.de:1967
2022-12-06T11:19:02Z
user-hzdr
user-health
user-fwm
user-fwc
user-rodare
Starke, Sebastian
Zwanenburg, Alex
Leger, Karoline
Zöphel, Klaus
Kotzerke, Jörg
Krause, Mechthild
Baumann, Michael
Troost, Esther Gera Cornelia
Löck, Steffen
2022-11-24
We include the input data, analysis scripts, analysis results and scripts to create the visualizations and plots used in the manuscript and supplement to our article "Longitudinal and multimodal radiomics models for head-and-neck cancer outcome prediction".
https://rodare.hzdr.de/record/1967
10.14278/rodare.1967
oai:rodare.hzdr.de:1967
url:https://www.hzdr.de/publications/Publ-35309
url:https://www.hzdr.de/publications/Publ-35560
doi:10.14278/rodare.1966
url:https://rodare.hzdr.de/communities/fwc
url:https://rodare.hzdr.de/communities/fwm
url:https://rodare.hzdr.de/communities/health
url:https://rodare.hzdr.de/communities/hzdr
url:https://rodare.hzdr.de/communities/rodare
info:eu-repo/semantics/closedAccess
radiomics
head-and-neck cancer
loco-regional control
survival analysis
computed tomography
positron emission tomography
cox proportional hazards
longitudinal imaging
Data publication: Longitudinal and multimodal radiomics models for head-and-neck cancer outcome prediction
info:eu-repo/semantics/other
other
oai:rodare.hzdr.de:558
2023-05-30T13:11:25Z
openaire_data
user-ecfunded
user-health
user-rodare
Müller, Johannes
Suckert, Theresa
Beyreuther, Elke
Schneider, Moritz
Boucsein, Marc
Bodenstein, Elisabeth
Stolz-Kieslich, Liane
Krause, Mechthild
von Neubeck, Cläre
Haase, Robert
Lühr, Armin
Dietrich, Antje
2021-01-20
The dataset contains comprehensive image data for a total of nine mice, which underwent normal tissue brain irradiation with 90 MeV protons.
In particular, the image data comprise cone-bem computed tomographies (CBCT), Monte Carlo beam transport simulations based on those CTs, regular magnetic resonance imaging (MRI) follow-up (≥ 26 weeks), a co-aligned DSURQE mouse brain atlas and scanned whole-brain tissue sections with histochemical and immunofluorescent markers for morphology (H&E), cell nuclei (DAPI), astrocytes (GFAP), microglia (Iba1), the intermediate filament protein Nestin, proliferation (Ki67), neurons (NeuN) and oligodendrocytes (OSP).
The volumetric image data (i.e. CBCT, MRI and brain atlas) were co-aligned using the ImageJ plugin Big Warp. The CBCT data was used as spatial reference to allow for mask-based, slice-wise alignment of CBCT and light microscopy image data in 3D with the scriptable registration tool Elastix.
We provide the data in raw format and as aligned data sets, as well as their spatial transformations.
https://rodare.hzdr.de/record/558
10.14278/rodare.558
oai:rodare.hzdr.de:558
eng
info:eu-repo/grantAgreement/EC/H2020/730983/
url:https://www.hzdr.de/publications/Publ-32124
doi:10.14278/rodare.557
url:https://rodare.hzdr.de/communities/ecfunded
url:https://rodare.hzdr.de/communities/health
url:https://rodare.hzdr.de/communities/rodare
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/4.0/legalcode
Preclinical
Image fusion
Proton radiation
Medical imaging
Histology
Slice2Volume: Fusion of multimodal medical imaging and light microscopy data of irradiation-injured brain tissue in 3D.
info:eu-repo/semantics/other
dataset
oai:rodare.hzdr.de:791
2023-05-30T13:11:25Z
openaire_data
user-ecfunded
user-health
user-rodare
Müller, Johannes
Suckert, Theresa
Beyreuther, Elke
Schneider, Moritz
Boucsein, Marc
Bodenstein, Elisabeth
Stolz-Kieslich, Liane
Krause, Mechthild
von Neubeck, Cläre
Haase, Robert
Lühr, Armin
Dietrich, Antje
2021-01-20
The dataset contains comprehensive image data for a total of nine mice, which underwent normal tissue brain irradiation with 90 MeV protons.
In particular, the image data comprise cone-bem computed tomographies (CBCT), Monte Carlo beam transport simulations based on those CTs, regular magnetic resonance imaging (MRI) follow-up (≥ 26 weeks), a co-aligned DSURQE mouse brain atlas and scanned whole-brain tissue sections with histochemical and immunofluorescent markers for morphology (H&E), cell nuclei (DAPI), astrocytes (GFAP), microglia (Iba1), the intermediate filament protein Nestin, proliferation (Ki67), neurons (NeuN) and oligodendrocytes (OSP).
The volumetric image data (i.e. CBCT, MRI and brain atlas) were co-aligned using the ImageJ plugin Big Warp. The CBCT data was used as spatial reference to allow for mask-based, slice-wise alignment of CBCT and light microscopy image data in 3D with the scriptable registration tool Elastix.
We provide the data in raw format and as aligned data sets, as well as their spatial transformations.
https://rodare.hzdr.de/record/791
10.14278/rodare.791
oai:rodare.hzdr.de:791
eng
info:eu-repo/grantAgreement/EC/H2020/730983/
url:https://www.hzdr.de/publications/Publ-32124
doi:10.14278/rodare.557
url:https://rodare.hzdr.de/communities/ecfunded
url:https://rodare.hzdr.de/communities/health
url:https://rodare.hzdr.de/communities/rodare
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/4.0/legalcode
Preclinical
Image fusion
Proton radiation
Medical imaging
Histology
Slice2Volume: Fusion of multimodal medical imaging and light microscopy data of irradiation-injured brain tissue in 3D.
info:eu-repo/semantics/other
dataset
oai:rodare.hzdr.de:810
2023-05-30T13:11:26Z
openaire_data
user-ecfunded
user-health
user-rodare
Müller, Johannes
Suckert, Theresa
Beyreuther, Elke
Schneider, Moritz
Boucsein, Marc
Bodenstein, Elisabeth
Stolz-Kieslich, Liane
Krause, Mechthild
von Neubeck, Cläre
Haase, Robert
Lühr, Armin
Dietrich, Antje
2021-01-20
The dataset contains comprehensive image data for a total of nine mice, which underwent normal tissue brain irradiation with 90 MeV protons.
In particular, the image data comprise cone-bem computed tomographies (CBCT), Monte Carlo beam transport simulations based on those CTs, regular magnetic resonance imaging (MRI) follow-up (≥ 26 weeks), a co-aligned DSURQE mouse brain atlas and scanned whole-brain tissue sections with histochemical and immunofluorescent markers for morphology (H&E), cell nuclei (DAPI), astrocytes (GFAP), microglia (Iba1), the intermediate filament protein Nestin, proliferation (Ki67), neurons (NeuN) and oligodendrocytes (OSP).
The volumetric image data (i.e. CBCT, MRI and brain atlas) were co-aligned using the ImageJ plugin Big Warp. The CBCT data was used as spatial reference to allow for mask-based, slice-wise alignment of CBCT and light microscopy image data in 3D with the scriptable registration tool Elastix.
We provide the data in raw format and as aligned data sets, as well as their spatial transformations.
https://rodare.hzdr.de/record/810
10.14278/rodare.810
oai:rodare.hzdr.de:810
eng
info:eu-repo/grantAgreement/EC/H2020/730983/
url:https://www.hzdr.de/publications/Publ-32124
doi:10.14278/rodare.557
url:https://rodare.hzdr.de/communities/ecfunded
url:https://rodare.hzdr.de/communities/health
url:https://rodare.hzdr.de/communities/rodare
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/4.0/legalcode
Preclinical
Image fusion
Proton radiation
Medical imaging
Histology
Slice2Volume: Fusion of multimodal medical imaging and light microscopy data of irradiation-injured brain tissue in 3D.
info:eu-repo/semantics/other
dataset
oai:rodare.hzdr.de:2644
2024-01-09T12:44:33Z
user-health
user-rodare
Peng, Xuan
Janićijević, Željko
Lemm, Sandy
Laube, Markus
Pietzsch, Jens
Bachmann, Michael
Baraban, Larysa
2024-01-09
summary over: (a) raw data for the metabolic assays (b) raw data for the analysis of the permeability of the capsules (c) unpublished images: typical cross section analysis of the capsules and organoids
https://rodare.hzdr.de/record/2644
10.14278/rodare.2644
oai:rodare.hzdr.de:2644
doi:10.22541/au.165830418.86011497/v1
url:https://www.hzdr.de/publications/Publ-35501
url:https://www.hzdr.de/publications/Publ-35494
doi:10.14278/rodare.2643
url:https://rodare.hzdr.de/communities/health
url:https://rodare.hzdr.de/communities/rodare
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/4.0/legalcode
Data publication: Shell engineering in soft alginate-based capsules for culturing liver spheroids
info:eu-repo/semantics/other
image-other
oai:rodare.hzdr.de:1501
2022-11-10T13:06:45Z
software
user-hzdr
user-health
user-rodare
Abdussalam, Wildan
2022-03-24
Software to synchonise the data between various data sources and casus database server. For Unix users please use MigrateWhere2test_0.7Unix.zip and for WIndows users please use MigrateWhere2test_0.7Win.zip. In order to use the scripts, please use the following instructions:
Windows
1. Create the postgreq sql database and set the port 5432
2. Create folder C:\Workspaces and unzip the unix file.
3. Create folder in workspaces, com.com.casus.env.where2test.migration\COM_CASUS_WHERE2TEST_MIGRATION and then unzip the source file inside COM_CASUS_WHERE2TEST_MIGRATION.
4. Set run Develop and run the .bat file on the folder MigrateWhere2test_0.7Unix to run in localhost.
Unix
1. Create PostgreSQL with port 32771.
2. Create folder /home/wildan/Workspaces and unzip the unix file.
3. Open the file MigrateWhere2test/MigrateWhere2test_run.sh and change the mode "Default" by "Production"
4. Create folder in workspaces, com.com.casus.env.where2test.migration.unix/COM_CASUS_WHERE2TEST_MIGRATION and then unzip the source file inside COM_CASUS_WHERE2TEST_MIGRATION.
5. run the MigrateWhere2test_run.sh in the "Production" mode.
https://rodare.hzdr.de/record/1501
10.14278/rodare.1501
oai:rodare.hzdr.de:1501
eng
url:https://www.hzdr.de/publications/Publ-34430
doi:10.14278/rodare.1500
url:https://rodare.hzdr.de/communities/health
url:https://rodare.hzdr.de/communities/hzdr
url:https://rodare.hzdr.de/communities/rodare
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/4.0/legalcode
data pipeline
Data synchronizator of Where2test pipeline
info:eu-repo/semantics/other
software
oai:rodare.hzdr.de:2473
2024-01-29T14:42:42Z
openaire_data
user-health
user-rodare
Liou, Natasha
De, Trina
Urbanski, Adrian
Khasriya, Rajvinder
Yakimovich, Artur
Horsley, Harry
2023-09-12
Urinary tract infections (UTI) are a common disorder. Its diagnosis can be made by microscopic examination of voided urine for cellular markers of infection. We present a dataset containing 300 images and 3,562 manually annotated urinary cells labelled into seven classes of clinically significant urinary content. It is an enriched dataset with samples acquired from the unstained and untreated urine of patients with symptomatic UTI. The aim of the dataset is to facilitate UTI diagnosis in nearly all clinical settings by using a simple imaging system which leverages advanced machine learning techniques.
Data acquisition
300 urine samples were obtained from patients with symptomatic UTI between April and August 2022 from a specialist LUTS outpatient clinic in central London. Urine samples were collected as natural voids and processed on-site within one hour to mitigate cellular degradation. Brightfield microscopic examination (Olympus BX41F microscope frame, U-5RE quintuple nosepiece, U-LS30 LED illuminator, U-AC Abbe condenser) was performed at x20 objective (Olympus PLCN20x Plan C N Achromat 20x/0.4). A disposable haemocytometer (C Chip™) was used for enumeration of red cells (RBC), white cells (WBC), epithelial cells (EPC), and the presence of other cellular content per 1 µl of urine by two experienced microscopists.
Images were acquired using the aforementioned brightfield microscope using a 0.5X C-mount adapter connected to a digital colour camera (Infinity 3S-1UR, Teledyne Lumenera). Images were taken in 16-bit colour in 1392 x 1040 .tif format using Capture and Analyse software. An enriched dataset approach was taken to maximise urinary cellular content in the acquired images. Such data curation was also necessary to overcome class imbalance. Daily Kohler illumination and global white balance was performed to ensure consistency in image acquisition.
Dataset annotation
300 images were acquired and manually annotated by first identifying cells of interest as a binary semantic segmentation task. Individual pixels were dichotomously labelled as either informative cells, foreground, or non-informative background. Non-informative background was further constrained by including unidentifiable cells, such as debris or grossly out-of-focus particles. Binary annotation was initially performed using ilastik, an open-source software using a Random Forest classifier for pixel classification, then manually refined at the pixel level to ensure accurate semantic segmentation. This produced a binary mask in 1392 x 1040 .tif format for each corresponding raw colour image.
Objects of interest were then manually labelled by two expert microscopists into one of seven clinically significant multi-class categories: rods, RBC/WBC, yeast, miscellaneous, single EPC, small EPC sheet, and large EPC sheet. This produced a multi-class mask in 1392 x 1040 .tif format with a label as pixel value from 0-7, where 0 is background (Table 1).
Data structure
The dataset is organised into three root folders: img (image), bin_mask (binary mask), and mult_mask (multi-class mask). Each folder has 300 files in .tif format and labelled with an incremental number.
Table1
Folder Files Objects Count Pixel Values
img 300 Raw data 0-255
bin_mask 300 Background/Foreground 0/1
mult_mask 300 Background/Class 0
Rod 1697 1
RBC/WBC 1056 2
Yeast 41 3
Miscellaneous 550 4
Single EPC 182 5
Small EPC sheet 26 6
Large EPC sheet 10 7
Total 3562
https://rodare.hzdr.de/record/2473
10.14278/rodare.2473
oai:rodare.hzdr.de:2473
eng
url:https://www.hzdr.de/publications/Publ-37531
doi:10.14278/rodare.2472
url:https://rodare.hzdr.de/communities/health
url:https://rodare.hzdr.de/communities/rodare
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/4.0/legalcode
clinical microscopy
urine microscopy
widefield
transmission light
image segmentation
binary segmentation
multiclass segmentation
Clinical urine microscopy for urinary tract infections
info:eu-repo/semantics/other
dataset
oai:rodare.hzdr.de:794
2023-05-30T13:11:26Z
openaire_data
user-ecfunded
user-health
user-rodare
Müller, Johannes
Suckert, Theresa
Beyreuther, Elke
Schneider, Moritz
Boucsein, Marc
Bodenstein, Elisabeth
Stolz-Kieslich, Liane
Krause, Mechthild
von Neubeck, Cläre
Haase, Robert
Lühr, Armin
Dietrich, Antje
2021-01-20
The dataset contains comprehensive image data for a total of nine mice, which underwent normal tissue brain irradiation with 90 MeV protons.
In particular, the image data comprise cone-bem computed tomographies (CBCT), Monte Carlo beam transport simulations based on those CTs, regular magnetic resonance imaging (MRI) follow-up (≥ 26 weeks), a co-aligned DSURQE mouse brain atlas and scanned whole-brain tissue sections with histochemical and immunofluorescent markers for morphology (H&E), cell nuclei (DAPI), astrocytes (GFAP), microglia (Iba1), the intermediate filament protein Nestin, proliferation (Ki67), neurons (NeuN) and oligodendrocytes (OSP).
The volumetric image data (i.e. CBCT, MRI and brain atlas) were co-aligned using the ImageJ plugin Big Warp. The CBCT data was used as spatial reference to allow for mask-based, slice-wise alignment of CBCT and light microscopy image data in 3D with the scriptable registration tool Elastix.
We provide the data in raw format and as aligned data sets, as well as their spatial transformations.
https://rodare.hzdr.de/record/794
10.14278/rodare.794
oai:rodare.hzdr.de:794
eng
info:eu-repo/grantAgreement/EC/H2020/730983/
url:https://www.hzdr.de/publications/Publ-32124
doi:10.14278/rodare.557
url:https://rodare.hzdr.de/communities/ecfunded
url:https://rodare.hzdr.de/communities/health
url:https://rodare.hzdr.de/communities/rodare
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/4.0/legalcode
Preclinical
Image fusion
Proton radiation
Medical imaging
Histology
Slice2Volume: Fusion of multimodal medical imaging and light microscopy data of irradiation-injured brain tissue in 3D.
info:eu-repo/semantics/other
dataset
oai:rodare.hzdr.de:1849
2024-01-23T09:14:11Z
openaire_data
user-ecfunded
user-health
user-rodare
Müller, Johannes
Suckert, Theresa
Beyreuther, Elke
Schneider, Moritz
Boucsein, Marc
Bodenstein, Elisabeth
Stolz-Kieslich, Liane
Krause, Mechthild
Neubeck, Cläre Von
Haase, Robert
Lühr, Armin
Dietrich, Antje
Nexhipi, Sindi
2022-09-21
The dataset contains comprehensive image data for a total of nine mice, which underwent normal tissue brain irradiation with 90 MeV protons.
In particular, the image data comprise cone-bem computed tomographies (CBCT), Monte Carlo beam transport simulations based on those CTs, regular magnetic resonance imaging (MRI) follow-up (≥ 26 weeks), a co-aligned DSURQE mouse brain atlas and scanned whole-brain tissue sections with histochemical and immunofluorescent markers for morphology (H&E), cell nuclei (DAPI), astrocytes (GFAP), microglia (Iba1), the intermediate filament protein Nestin, proliferation (Ki67), neurons (NeuN) and oligodendrocytes (OSP).
The volumetric image data (i.e. CBCT, MRI and brain atlas) were co-aligned using the ImageJ plugin Big Warp. The CBCT data was used as spatial reference to allow for mask-based, slice-wise alignment of CBCT and light microscopy image data in 3D with the scriptable registration tool Elastix.
We provide the data in raw format and as aligned data sets, as well as their spatial transformations.
Note: There are ongoing corrections taking place with the B6 mouse strain data (P2A_B6_M1, P2A_B6_M2, P2A_B6_M6, P2A_B6_M10). If you are interested in working with these data, please wait for the new version to be uploaded or contact the authors of https://doi.org/10.1016/j.radonc.2023.109591
Chunked zip: The histological data are stored as chunked .zip files (*.zip.001 - *.zip.0XX). In order to unpack the data, download all chunks into the same directory, then unpack.
https://rodare.hzdr.de/record/1849
10.14278/rodare.1849
oai:rodare.hzdr.de:1849
eng
info:eu-repo/grantAgreement/EC/H2020/730983/
doi:10.3389/fonc.2020.598360
url:https://www.hzdr.de/publications/Publ-31469
url:https://www.hzdr.de/publications/Publ-32124
url:https://www.hzdr.de/publications/Publ-32394
url:https://www.hzdr.de/publications/Publ-32394
doi:10.1016/j.radonc.2023.109591
doi:10.14278/rodare.557
url:https://rodare.hzdr.de/communities/ecfunded
url:https://rodare.hzdr.de/communities/health
url:https://rodare.hzdr.de/communities/rodare
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/4.0/legalcode
Preclinical
Image fusion
Proton radiation
Medical imaging
Histology
Slice2Volume: Fusion of multimodal medical imaging and light microscopy data of irradiation-injured brain tissue in 3D.
info:eu-repo/semantics/other
dataset