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Metadata

Name
Code and Data from: Segmenting Root Systems in X-Ray Computed Tomography Images Using Level Sets
Repository
ZENODO
Identifier
doi:10.5281/zenodo.3333709
Description
This record contains code and data for segmentation using a three-dimensional level-set method, written by Amy Tabb in C++.&nbsp; The record also contains two datasets of root systems in media imaged with X-Ray CT, and the results of running the code on those datasets.&nbsp; The code will also perform a pre-processing task in three-dimensional image sets, and a dataset for that purpose is included as well.&nbsp; This work is a companion to the paper : &quot;Segmenting root systems in X-ray computed tomography images using level sets&quot; (WACV 2018) by the authors or this record, and and open-access version of the paper is here -- https://arxiv.org/abs/1809.06398 .&nbsp;&nbsp; The code is also available from Github: https://github.com/amy-tabb/tabb-level-set-segmentation , using a DOI and stable releases https://doi.org/10.5281/zenodo.3344906.

Format of the data:

Three input datasets are provided; two for the segmentation functionality of the code, and one to test the pre-processing functionality.&nbsp; The two segmentation sets are the same as were used in the paper, and are CassavaDataset, and SoybeanDataset.&nbsp; The pre-processing set is CassavaSlices.&nbsp; The output set for Soybean is SoybeanResultsJul11.&nbsp; The Cassava result set is large, so I broke it into three compressed folders, CassavaResultsJul12_A, _B, _C.&nbsp; _B is the largest, and only contains the results overwritten on the original X-Ray images.&nbsp; Unless your connection to Zenodo is extremely fast, it will be faster to compute the result than to download it.

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Data or Study Types
multiple
Source Organization
Unknown
Access Conditions
available
Year
2019
Access Hyperlink
https://doi.org/10.5281/zenodo.3333709

Distributions

  • Encoding Format: HTML ; URL: https://doi.org/10.5281/zenodo.3333709
This project was funded in part by grant U24AI117966 from the NIH National Institute of Allergy and Infectious Diseases as part of the Big Data to Knowledge program. We thank all members of the bioCADDIE community for their valuable input on the overall project.