Data for The utility of transfer learning to improve the performance of deep learning in axon segmentation

Oostrom, Marjolein T; Muniak, Michael; Eichler West, Rogene M; Akers, Sarah M; Pande, Paritosh; Obiri, Moses Y; Wang, Wei; Bowyer, Kasey; Wu, Zhuhao; Bramer, Lisa M; Webb-Robertson, Bobbie-Jo M; Mao, Tianyi

doi:10.25584/2308772

Title: Data for The utility of transfer learning to improve the performance of deep learning in axon segmentation

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Abstract

The utility of transfer learning to improve the performance of deep learning in axon segmentation Data 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, which was created by Albert Pun and Drew Friedmann Acknowledgments MO, RMEW, SA, MO, LB, 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 weremore »« less

Authors:

Oostrom, Marjolein T; Muniak, Michael; Eichler West, Rogene M; Akers, Sarah M; Pande, Paritosh; Obiri, Moses Y; Wang, Wei; Bowyer, Kasey; Wu, Zhuhao; Bramer, Lisa M; Webb-Robertson, Bobbie-Jo M; Mao, Tianyi

Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); PNNL
Oregon Health & Science Univ., Portland, OR (United States)
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Cornell Univ., Ithaca, NY (United States)

Publication Date:: Mon Feb 05 23:00:00 EST 2024

DOE Contract Number:: AC05-76RL01830

Research Org.:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Org.:: USDOE Office of Science (SC)

OSTI Identifier:: 2308772

DOI:: https://doi.org/10.25584/2308772

Citation Formats


                    Oostrom, Marjolein T, Muniak, Michael, Eichler West, Rogene M, Akers, Sarah M, Pande, Paritosh, Obiri, Moses Y, Wang, Wei, Bowyer, Kasey, Wu, Zhuhao, Bramer, Lisa M, Webb-Robertson, Bobbie-Jo M, and Mao, Tianyi. Data for The utility of transfer learning to improve the performance of deep learning in axon segmentation.  United States: N. p., 2024. 
        Web.  doi:10.25584/2308772.

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                    Oostrom, Marjolein T, Muniak, Michael, Eichler West, Rogene M, Akers, Sarah M, Pande, Paritosh, Obiri, Moses Y, Wang, Wei, Bowyer, Kasey, Wu, Zhuhao, Bramer, Lisa M, Webb-Robertson, Bobbie-Jo M, & Mao, Tianyi. Data for The utility of transfer learning to improve the performance of deep learning in axon segmentation.  United States.  doi:https://doi.org/10.25584/2308772

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                    Oostrom, Marjolein T, Muniak, Michael, Eichler West, Rogene M, Akers, Sarah M, Pande, Paritosh, Obiri, Moses Y, Wang, Wei, Bowyer, Kasey, Wu, Zhuhao, Bramer, Lisa M, Webb-Robertson, Bobbie-Jo M, and Mao, Tianyi. 2024.  
"Data for The utility of transfer learning to improve the performance of deep learning in axon segmentation".  United States.  doi:https://doi.org/10.25584/2308772.  https://www.osti.gov/servlets/purl/2308772. Pub date:Mon Feb 05 23:00:00 EST 2024

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@article{osti_2308772,

  title        = {Data for The utility of transfer learning to improve the performance of deep learning in axon segmentation},

  author       = {Oostrom, Marjolein T and Muniak, Michael and Eichler West, Rogene M and Akers, Sarah M and Pande, Paritosh and Obiri, Moses Y and Wang, Wei and Bowyer, Kasey and Wu, Zhuhao and Bramer, Lisa M and Webb-Robertson, Bobbie-Jo M and Mao, Tianyi},

  abstractNote = {The utility of transfer learning to improve the performance of deep learning in axon segmentation Data 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, which was created by Albert Pun and Drew Friedmann Acknowledgments MO, RMEW, SA, MO, LB, 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. This research is affiliated with the Pacific northwest bioMedical Innovation Co-laboratory (PMedIC) collaboration between OHSU and PNNL.},

  doi          = {10.25584/2308772},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Mon Feb 05 23:00:00 EST 2024},

  month        = {Mon Feb 05 23:00:00 EST 2024}

}

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DOI: https://doi.org/10.25584/2308772

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