Dataset of Fluorescent Nuclear Track Detector images for neutron dosimetry
Description
This dataset was originally generated in a previous publication: Schmidt, S., Christensen, J.B., Lutz, B., Stabilini, A., Yukihara, E.G., Vedelago, J., 2025. Sensitivity analysis of fluorescent nuclear track detectors for fast and high-energy mono-energetic neutron dosimetry. Medical Physics 52, e17799. https://doi.org/10.1002/mp.17799. This dataset was also used in: Thai, L.-Y. J., Schmidt, S., Walter, A., Häcker, R.V. , Giske, K., Vedelago, J., 2026. Capability of deep learning to predict recoil protons for neutron dosimetry with Fluorescent Nuclear Track Detectors. Radiation Measurements, 107662. https://doi.org/10.1016/j.radmeas.2026.107662. This dataset contains raw images and binary reference label masks for the training and testing. The training dataset comprises of all six mono-energetic neutron cases: - 1800 raw images can be found in "imagesTr", - 1800 binary reference label masks can be found in "labelsTr". The test dataset comprises of all seven ambient dose equivalent H*(10) values for the 241Am-Be neutron source: - 2100 raw images can be found in "imagesTs", - 2100 binary reference label masks can be found in "labelsTs". The seven H*(10) values were separated into subfolders: - "ss0643_44_45" -> 0 mSv, - "ss0610_11_12" -> 1 mSv, - "ss0613_14_15" -> 5 mSv, - "ss0616_17_18" -> 10 mSv, - "ss0637_38_39" -> 15 mSv, - "ss0601_02_03" -> 50 mSv, - "ss0604_05_06" -> 100 mSv. Note: In order to properly visualize the 16-bit images, intensity-scaling (e.g. with ImageJ/FIJI) might be required. Otherwise, the images might appear black.
Files
Steps to reproduce
To reproduce the results of Thai et al. (2026) see the software related to this dataset.
Institutions
- Deutsches KrebsforschungszentrumBaden-Württemberg, Heidelberg
- UniversitatsKlinikum HeidelbergBaden-Württemberg, Heidelberg
- Heidelberg UniversityBaden-Wurttemberg, Heidelberg