Pre-trained nnU-Net 3d_fullres model for mouse thorax segmentation
Authors/Creators
- 1. Department of Radiation Oncology, University Medical Center Groningen
- 2. Department of Radiation Science and Technology, Delft University of Technology
- 3. Department of Medical Biology, Amsterdam University Medical Centers (Location AMC) and Cancer Center Amsterdam
- 4. Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Center
- 5. Department of Radiology, Leiden University Medical Center
Description
This repository contains a pre-trained nnU-Net 3d_fullres model for mouse thorax segmentation. This model was trained using native microCT images taken from a publicly available preclinical micro-CT database [1]. The corresponding annotations for the heart, spinal cord, right and left lungs can be found at https://doi.org/10.5281/zenodo.5121272. In order to use the model, nnU-Net has to be installed. Instructions can be found at https://github.com/MIC-DKFZ/nnUNet.
In case that you find this model useful for your research, please cite the original work:
Malimban, J., Lathouwers, D., Qian, H. et al. Deep learning-based segmentation of the thorax in mouse micro-CT scans. Sci Rep 12, 1822 (2022). https://doi.org/10.1038/s41598-022-05868-7
[1] Rosenhain S, Magnuska Z A, Yamoah G G, Rawashdeh W A, Kiessling F and Gremse F 2018 A preclinical micro-computed tomography database including 3D whole body organ segmentations Online: https://springernature.figshare.com/collections/A_preclinical_micro-computed_tomography_database_including_3D_whole_body_organ_segmentations/4224377/1
Files
Task101_LungOAR_3d.zip
Files
(1.1 GB)
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