[REVIEW]: Volume Segmantics: A Python Package for Semantic Segmentation of Volumetric Data Using Pre-trained PyTorch Deep Learning Models #4691

editorialbot · 2022-08-21T04:37:15Z

Submitting author: @OllyK (Oliver N. F. King)
Repository: https://github.com/DiamondLightSource/volume-segmantics
Branch with paper.md (empty if default branch): paper
Version: v0.2.7
Editor: @osorensen
Reviewers: @jingpengw, @estenhl
Archive: 10.5281/zenodo.7143363

Status

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Markdown: [![status](https://joss.theoj.org/papers/cb6e86a01006b487a09e41a9fc5b4e5e/status.svg)](https://joss.theoj.org/papers/cb6e86a01006b487a09e41a9fc5b4e5e)

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Reviewer instructions & questions

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Checklists

📝 Checklist for @estenhl

📝 Checklist for @jingpengw

The text was updated successfully, but these errors were encountered:

editorialbot · 2022-08-21T04:37:16Z

Hello humans, I'm @editorialbot, a robot that can help you with some common editorial tasks.

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For example, to regenerate the paper pdf after making changes in the paper's md or bib files, type:

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editorialbot · 2022-08-21T04:37:18Z

Software report:

github.com/AlDanial/cloc v 1.88  T=0.10 s (558.2 files/s, 85401.7 lines/s)
-------------------------------------------------------------------------------
Language                     files          blank        comment           code
-------------------------------------------------------------------------------
HTML                             5            551              0           3284
Python                          36            602            517           2555
Markdown                         6            140              0            471
YAML                             6             12             27            198
TeX                              1              7              0            103
TOML                             1              5              0             50
JavaScript                       1              3              3             40
-------------------------------------------------------------------------------
SUM:                            56           1320            547           6701
-------------------------------------------------------------------------------


gitinspector failed to run statistical information for the repository

editorialbot · 2022-08-21T04:37:19Z

Wordcount for paper.md is 989

editorialbot · 2022-08-21T04:37:28Z

Reference check summary (note 'MISSING' DOIs are suggestions that need verification):

OK DOIs

- 10.1007/s11263-015-0816-y is OK
- 10.1007/978-3-030-32245-8_4 is OK
- 10.3390/info11020125 is OK
- 10.1002/essoar.10506807.2 is OK
- 10.1098/rsif.2021.0140 is OK
- 10.3389/fcell.2022.842342 is OK

MISSING DOIs

- None

INVALID DOIs

- None

editorialbot · 2022-08-21T04:38:39Z

👉📄 Download article proof 📄 View article proof on GitHub 📄 👈

osorensen · 2022-09-09T06:15:25Z

👋 @jingpengw, @estenhl, could you please update us on how it's going with your reviews?

estenhl · 2022-09-09T08:00:03Z

👋 @jingpengw, @estenhl, could you please update us on how it's going with your reviews?

Will finish it sometime next week

estenhl · 2022-09-09T08:00:37Z

Review checklist for @estenhl

Conflict of interest

I confirm that I have read the JOSS conflict of interest (COI) policy and that: I have no COIs with reviewing this work or that any perceived COIs have been waived by JOSS for the purpose of this review.

Code of Conduct

I confirm that I read and will adhere to the JOSS code of conduct.

General checks

Repository: Is the source code for this software available at the https://github.com/DiamondLightSource/volume-segmantics?
License: Does the repository contain a plain-text LICENSE file with the contents of an OSI approved software license?
Contribution and authorship: Has the submitting author (@OllyK) made major contributions to the software? Does the full list of paper authors seem appropriate and complete?
Substantial scholarly effort: Does this submission meet the scope eligibility described in the JOSS guidelines
Data sharing: If the paper contains original data, data are accessible to the reviewers. If the paper contains no original data, please check this item.
Reproducibility: If the paper contains original results, results are entirely reproducible by reviewers. If the paper contains no original results, please check this item.
Human and animal research: If the paper contains original data research on humans subjects or animals, does it comply with JOSS's human participants research policy and/or animal research policy? If the paper contains no such data, please check this item.

Functionality

Installation: Does installation proceed as outlined in the documentation?
Functionality: Have the functional claims of the software been confirmed?
Performance: If there are any performance claims of the software, have they been confirmed? (If there are no claims, please check off this item.)

Documentation

A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
Installation instructions: Is there a clearly-stated list of dependencies? Ideally these should be handled with an automated package management solution.
Example usage: Do the authors include examples of how to use the software (ideally to solve real-world analysis problems).
Functionality documentation: Is the core functionality of the software documented to a satisfactory level (e.g., API method documentation)?
Automated tests: Are there automated tests or manual steps described so that the functionality of the software can be verified?
Community guidelines: Are there clear guidelines for third parties wishing to 1) Contribute to the software 2) Report issues or problems with the software 3) Seek support

Software paper

Summary: Has a clear description of the high-level functionality and purpose of the software for a diverse, non-specialist audience been provided?
A statement of need: Does the paper have a section titled 'Statement of need' that clearly states what problems the software is designed to solve, who the target audience is, and its relation to other work?
State of the field: Do the authors describe how this software compares to other commonly-used packages?
Quality of writing: Is the paper well written (i.e., it does not require editing for structure, language, or writing quality)?
References: Is the list of references complete, and is everything cited appropriately that should be cited (e.g., papers, datasets, software)? Do references in the text use the proper citation syntax?

xiuliren · 2022-09-09T15:35:05Z

Review checklist for @jingpengw

Conflict of interest

I confirm that I have read the JOSS conflict of interest (COI) policy and that: I have no COIs with reviewing this work or that any perceived COIs have been waived by JOSS for the purpose of this review.

Code of Conduct

I confirm that I read and will adhere to the JOSS code of conduct.

General checks

Repository: Is the source code for this software available at the https://github.com/DiamondLightSource/volume-segmantics?
License: Does the repository contain a plain-text LICENSE file with the contents of an OSI approved software license?
Contribution and authorship: Has the submitting author (@OllyK) made major contributions to the software? Does the full list of paper authors seem appropriate and complete?
Substantial scholarly effort: Does this submission meet the scope eligibility described in the JOSS guidelines
Data sharing: If the paper contains original data, data are accessible to the reviewers. If the paper contains no original data, please check this item.
Reproducibility: If the paper contains original results, results are entirely reproducible by reviewers. If the paper contains no original results, please check this item.
Human and animal research: If the paper contains original data research on humans subjects or animals, does it comply with JOSS's human participants research policy and/or animal research policy? If the paper contains no such data, please check this item.

Functionality

Installation: Does installation proceed as outlined in the documentation?
Functionality: Have the functional claims of the software been confirmed?
Performance: If there are any performance claims of the software, have they been confirmed? (If there are no claims, please check off this item.)

Documentation

A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
Installation instructions: Is there a clearly-stated list of dependencies? Ideally these should be handled with an automated package management solution.
Example usage: Do the authors include examples of how to use the software (ideally to solve real-world analysis problems).
Functionality documentation: Is the core functionality of the software documented to a satisfactory level (e.g., API method documentation)?
Automated tests: Are there automated tests or manual steps described so that the functionality of the software can be verified?
Community guidelines: Are there clear guidelines for third parties wishing to 1) Contribute to the software 2) Report issues or problems with the software 3) Seek support

Software paper

Summary: Has a clear description of the high-level functionality and purpose of the software for a diverse, non-specialist audience been provided?
A statement of need: Does the paper have a section titled 'Statement of need' that clearly states what problems the software is designed to solve, who the target audience is, and its relation to other work?
State of the field: Do the authors describe how this software compares to other commonly-used packages?
Quality of writing: Is the paper well written (i.e., it does not require editing for structure, language, or writing quality)?
References: Is the list of references complete, and is everything cited appropriately that should be cited (e.g., papers, datasets, software)? Do references in the text use the proper citation syntax?

xiuliren · 2022-09-09T15:44:46Z

@osorensen
@OllyK
It seems that the State of the field is missing. It would be better to compare with existing tools including my own:
https://github.com/flatironinstitute/neutorch

I have also listed some others in the README of my repo.
DeepEM
DataProvider3
PyTorchUtils
pytorch_connectomics

As such, the reference list is not complete from this perspective.

OllyK · 2022-09-09T16:42:29Z

Hi @jingpengw Thank you for taking the time to look at this. Sure, good point, I'll add this section next week and let you know when it's ready.

OllyK · 2022-09-14T12:37:22Z

Hi @jingpengw I've made some changes to the paper now as requested. I'll ask editorialbot to regenerate the PDF and check references again, just in case. Best wishes OllyK

OllyK · 2022-09-14T12:37:31Z

@editorialbot generate pdf

editorialbot · 2022-09-14T12:39:11Z

👉📄 Download article proof 📄 View article proof on GitHub 📄 👈

OllyK · 2022-09-14T12:42:46Z

@editorialbot check repository

editorialbot · 2022-09-14T12:42:50Z

Software report:

github.com/AlDanial/cloc v 1.88  T=0.10 s (562.2 files/s, 87155.5 lines/s)
-------------------------------------------------------------------------------
Language                     files          blank        comment           code
-------------------------------------------------------------------------------
HTML                             5            551              0           3284
Python                          36            602            517           2555
Markdown                         6            145              0            475
TeX                              1             17              0            198
YAML                             6             12             27            198
TOML                             1              5              0             50
JavaScript                       1              3              3             40
-------------------------------------------------------------------------------
SUM:                            56           1335            547           6800
-------------------------------------------------------------------------------


gitinspector failed to run statistical information for the repository

editorialbot · 2022-09-14T12:42:51Z

Wordcount for paper.md is 1216

OllyK · 2022-09-22T10:09:49Z

Hi @osorensen , according to the docs, only editors can run the editorialbot check references command. Would it be possible for you to run it? - I updated the references last week. Kind regards

osorensen · 2022-09-22T10:42:34Z

@editorialbot check references

editorialbot · 2022-09-22T10:43:54Z

Reference check summary (note 'MISSING' DOIs are suggestions that need verification):

OK DOIs

- 10.1007/s11263-015-0816-y is OK
- 10.1007/978-3-030-32245-8_4 is OK
- 10.3390/info11020125 is OK
- 10.1002/essoar.10506807.2 is OK
- 10.1098/rsif.2021.0140 is OK
- 10.3389/fcell.2022.842342 is OK
- 10.48550/arXiv.1706.05587 is OK
- 10.1038/s41598-019-55431-0 is OK

MISSING DOIs

- None

INVALID DOIs

- None

osorensen · 2022-09-26T13:23:30Z

@jingpengw, could you please let us now if the last edits by the authors address the remaining issues on your checklist?

osorensen · 2022-09-26T13:26:54Z

@estenhl, thanks for your review!

Is the missing check in the Reproducibility section related to the results reported in this section of the paper?

xiuliren · 2022-09-26T14:28:35Z

thank you! I think it is good to go!

OllyK · 2022-10-03T11:22:48Z

@estenhl Feel free to ask if there's anything that needs clarifying or updating. The paper doesn't contain any original results, it references studies where we've used earlier versions of the code to segment data. Best Wishes.

osorensen · 2022-10-05T11:44:55Z

@editorialbot generate pdf

osorensen · 2022-10-05T11:45:05Z

@editorialbot check references

editorialbot · 2022-10-05T11:46:16Z

Reference check summary (note 'MISSING' DOIs are suggestions that need verification):

OK DOIs

- 10.1007/s11263-015-0816-y is OK
- 10.1007/978-3-030-32245-8_4 is OK
- 10.3390/info11020125 is OK
- 10.1002/essoar.10506807.2 is OK
- 10.1098/rsif.2021.0140 is OK
- 10.3389/fcell.2022.842342 is OK
- 10.48550/arXiv.1706.05587 is OK
- 10.48550/arXiv.2112.05754 is OK
- 10.1038/s41598-019-55431-0 is OK

MISSING DOIs

- None

INVALID DOIs

- None

editorialbot · 2022-10-05T11:46:34Z

👉📄 Download article proof 📄 View article proof on GitHub 📄 👈

osorensen · 2022-10-05T11:49:30Z

@editorialbot recommend-accept

editorialbot · 2022-10-05T11:49:31Z

Attempting dry run of processing paper acceptance...

editorialbot · 2022-10-05T11:50:58Z

Reference check summary (note 'MISSING' DOIs are suggestions that need verification):

OK DOIs

- 10.1007/s11263-015-0816-y is OK
- 10.1007/978-3-030-32245-8_4 is OK
- 10.3390/info11020125 is OK
- 10.1002/essoar.10506807.2 is OK
- 10.1098/rsif.2021.0140 is OK
- 10.3389/fcell.2022.842342 is OK
- 10.48550/arXiv.1706.05587 is OK
- 10.48550/arXiv.2112.05754 is OK
- 10.1038/s41598-019-55431-0 is OK

MISSING DOIs

- None

INVALID DOIs

- None

editorialbot · 2022-10-05T11:51:17Z

👋 @openjournals/joss-eics, this paper is ready to be accepted and published.

Check final proof 👉📄 Download article

If the paper PDF and the deposit XML files look good in openjournals/joss-papers#3581, then you can now move forward with accepting the submission by compiling again with the command @editorialbot accept

OllyK · 2022-10-05T12:39:25Z

🎉 Thanks again @estenhl @jingpengw @osorensen . It's been a pleasure.

arfon · 2022-10-09T08:05:23Z

@editorialbot accept

editorialbot · 2022-10-09T08:05:24Z

Doing it live! Attempting automated processing of paper acceptance...

editorialbot · 2022-10-09T08:07:02Z

🐦🐦🐦 👉 Tweet for this paper 👈 🐦🐦🐦

editorialbot · 2022-10-09T08:07:03Z

🚨🚨🚨 THIS IS NOT A DRILL, YOU HAVE JUST ACCEPTED A PAPER INTO JOSS! 🚨🚨🚨

Here's what you must now do:

Check final PDF and Crossref metadata that was deposited 👉 Creating pull request for 10.21105.joss.04691 joss-papers#3592
Wait a couple of minutes, then verify that the paper DOI resolves https://doi.org/10.21105/joss.04691
If everything looks good, then close this review issue.
Party like you just published a paper! 🎉🌈🦄💃👻🤘

Any issues? Notify your editorial technical team...

OllyK · 2022-10-09T10:35:52Z

Brilliant! 👍 🎉 🍨 Thanks all!
@arfon One minor thing is that the link associated with my name is broken. Not a big issue but pointing it out in case it's an easy fix.

arfon · 2022-10-09T12:26:59Z

@OllyK – the link works for me? https://joss.theoj.org/papers/by/Oliver%20N%2E%20f%2E%20King

What browser are you using out of interest?

editorialbot · 2022-10-09T12:27:01Z

🎉🎉🎉 Congratulations on your paper acceptance! 🎉🎉🎉

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   :target: https://doi.org/10.21105/joss.04691

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The Journal of Open Source Software is a community-run journal and relies upon volunteer effort. If you'd like to support us please consider doing either one (or both) of the the following:

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arfon · 2022-10-09T12:27:25Z

@jingpengw, @estenhl – many thanks for your reviews here and to @osorensen for editing this submission! JOSS relies upon the volunteer effort of people like you and we simply wouldn't be able to do this without you ✨

@OllyK – your paper is now accepted and published in JOSS ⚡🚀💥

editorialbot · 2022-10-09T12:27:26Z

🎉🎉🎉 Congratulations on your paper acceptance! 🎉🎉🎉

If you would like to include a link to your paper from your README use the following code snippets:

Markdown:
[![DOI](https://joss.theoj.org/papers/10.21105/joss.04691/status.svg)](https://doi.org/10.21105/joss.04691)

HTML:
<a style="border-width:0" href="https://doi.org/10.21105/joss.04691">
  <img src="https://joss.theoj.org/papers/10.21105/joss.04691/status.svg" alt="DOI badge" >
</a>

reStructuredText:
.. image:: https://joss.theoj.org/papers/10.21105/joss.04691/status.svg
   :target: https://doi.org/10.21105/joss.04691

This is how it will look in your documentation:

We need your help!

The Journal of Open Source Software is a community-run journal and relies upon volunteer effort. If you'd like to support us please consider doing either one (or both) of the the following:

Volunteering to review for us sometime in the future. You can add your name to the reviewer list here: https://joss.theoj.org/reviewer-signup.html
Making a small donation to support our running costs here: https://numfocus.org/donate-to-joss

OllyK · 2022-10-09T12:45:23Z

@OllyK – the link works for me? https://joss.theoj.org/papers/by/Oliver%20N%2E%20f%2E%20King

What browser are you using out of interest?
@arfon Strangely the link works in Firefox but doesn't work in Chrome (on Windows) or on a DuckDuckGo browser on my Android phone. Like I said, not a big deal, happy it's been published 😄. Thanks @osorensen @estenhl and @jingpengw for taking the time to review!

editorialbot added Python review TeX Track: 5 (DSAIS) Data Science, Artificial Intelligence, and Machine Learning labels Aug 21, 2022

editorialbot assigned osorensen Aug 21, 2022

editorialbot mentioned this issue Aug 21, 2022

[PRE REVIEW]: Volume Segmantics: A Python Package for Semantic Segmentation of Volumetric Data Using Pre-trained PyTorch Deep Learning Models #4645

Closed

editorialbot added the recommend-accept Papers recommended for acceptance in JOSS. label Oct 5, 2022

editorialbot added accepted published Papers published in JOSS labels Oct 9, 2022

arfon closed this as completed Oct 9, 2022

arfon reopened this Oct 9, 2022

arfon closed this as completed Oct 9, 2022

editorialbot mentioned this issue Oct 1, 2023

[PRE REVIEW]: TrackSegNet: a tool for trajectory segmentation into diffusive states using supervised deep learning #5457

Closed

editorialbot mentioned this issue Dec 1, 2023

[PRE REVIEW]: Segment-Lidar: Automatic Unsupervised LiDAR-Segmentation using Segment-Anything Model (SAM) #6107

Closed

editorialbot mentioned this issue Oct 7, 2024

[PRE REVIEW]: RT-utils: A Minimal Python Library for RT-struct Manipulation #7056

Closed

This was referenced Nov 12, 2024

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[PRE REVIEW]: pySegmentationUpsampler: Resampling segmented medical image data for treatment planning in ultrasound therapy #7463

Open

[REVIEW]: Volume Segmantics: A Python Package for Semantic Segmentation of Volumetric Data Using Pre-trained PyTorch Deep Learning Models #4691

[REVIEW]: Volume Segmantics: A Python Package for Semantic Segmentation of Volumetric Data Using Pre-trained PyTorch Deep Learning Models #4691

Comments

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Review checklist for @estenhl

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General checks

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Documentation

Software paper

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Review checklist for @jingpengw

Conflict of interest

Code of Conduct

General checks

Functionality

Documentation

Software paper

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