Description
The utility of transfer learning to improve the performance of deep learning in axon segmentation Data
NOTE: If the download button doesn't work, please find the data here.
Data: All the input and labeled volumes
tf-logs: Tensorflow logs, view with command "tensorboard --logdir [name of folder]"
Model Weights: model_weights: the argument list under variable combo indicate 1) no oversampling, 2) no rotation, 3) no learn scheduler, and 4) flipping on all three dimensions, and the additional values indicate 5) elastic deformation percentage, 6) rotate deformation percentage, 7) layer setting , 8) learning rate, and 9) training/validation/test data division suffix (leave '' if not using suffix).
Results: Output from inference
segment_total_results_validation_final: All validation results and calculations
segment_total_results: All test results and calculations
## Authors
The modified code was created for a paper by:
Marjolein Oostrom, Michael A. Muniak, Rogene Eichler West, Sarah Akers, Paritosh Pande, Moses Obiri, Wei Wang, Kasey Bowyer, Zhuhao Wu, Lisa Bramer, Tianyi Mao, Bobbie Jo Webb-Robertson
The work is adapted from [Github TrailMap](https://github.com/AlbertPun/TRAILMAP), which was created by Albert Pun and Drew Friedmann
## Citing
Modified code:
Oostrom, Marjolein, Michael A. Muniak, Rogene M. Eichler West, Sarah Akers, Paritosh Pande, Moses Obiri, Wei Wang et al. "Fine-tuning TrailMap: The utility of transfer learning to improve the performance of deep learning in axon segmentation of light-sheet microscopy images." PloS one 19, no. 3 (2024): e0293856.
Original TrailMap:
Friedmann D, Pun A, Adams EL, Lui JH, Kebschull JM, Grutzner SM, et al. Mapping mesoscale axonal projections in the mouse brain using a 3D convolutional network. Proceedings of the National Academy of Sciences. 2020;117(20):11068–75. pmid:32358193
## Acknowledgments
MTO, RMEW, SA, MO, LMB, BJWR were supported by the Laboratory Directed Research and Development at Pacific Northwest National Laboratory (PNNL), a Department of Energy facility operated by Battelle under contract DE-AC05-76RLO01830. WW, KB, and ZW were supported in part by a NIH/BRAIN Initiative Grant RF1MH128969. MAM and TM were supported by two NIH/BRAIN Initiative Grants R01NS104944, RF1MH120119 and NIH R01NS081071.
We thank Amelia Culp for assistance with animal injections, and Dr. Patricia Jensen for providing DbhCre mice. This research is affiliated with the Pacific northwest bioMedical Innovation Co-laboraoty (PMedIC) joint research collaboration between Pacific Northwest National Laboratory (PNNL) and Oregon Health & Science University (OHSU).
## Disclaimer
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Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.
PACIFIC NORTHWEST NATIONAL LABORATORY
operated by
BATTELLE
for the
UNITED STATES DEPARTMENT OF ENERGY
under Contract DE-AC05-76RL01830
## Copyright
Simplified BSD
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