ALL Metrics
-
Views
-
Downloads
Get PDF
Get XML
Cite
Export
Track
Software Tool Article
Revised

A multi-spectral myelin annotation tool for machine learning based myelin quantification

[version 4; peer review: 2 approved]
PUBLISHED 15 Nov 2023
Author details Author details
OPEN PEER REVIEW
REVIEWER STATUS

This article is included in the NEUBIAS - the Bioimage Analysts Network gateway.

This article is included in the Artificial Intelligence and Machine Learning gateway.

Abstract

Myelin is an essential component of the nervous system and myelin damage causes demyelination diseases. Myelin is a sheet of oligodendrocyte membrane wrapped around the neuronal axon. In the fluorescent images, experts manually identify myelin by co-localization of oligodendrocyte and axonal membranes that fit certain shape and size criteria. Because myelin wriggles along x-y-z axes, machine learning is ideal for its segmentation. However, machine-learning methods, especially convolutional neural networks (CNNs), require a high number of annotated images, which necessitate expert labor. To facilitate myelin annotation, we developed a workflow and software for myelin ground truth extraction from multi-spectral fluorescent images. Additionally, to the best of our knowledge, for the first time, a set of annotated myelin ground truths for machine learning applications were shared with the community.

Keywords

myelin annotation tool, myelin quantification, fluorescence images, machine learning, image analysis

Revised Amendments from Version 3

This latest version emphasizes the expertise levels of the experts performing myelin marking and discusses the marking capabilities provided by CEMotate to these experts.

See the authors' detailed response to the review by Predrag Janjic

Introduction

Myelin degeneration causes neurodegenerative disorders, such as multiple sclerosis (MS)1,2. There are no remyelinating drugs. Myelin quantification is essential for drug discovery, which often involves screening thousands of compounds3. Currently, fluorescent myelin quantification is manual, and labor-intensive. Automation of quantification using machine learning can facilitate drug discovery by reducing time and labor costs4. However, myelin annotation suffers the same limitations as manual quantification. To assist researchers and bioimage analysts, we developed a workflow and software for myelin ground truth extraction from multi-spectral fluorescent images.

Myelin is formed by oligodendrocytes wrapping the axons5. It is identified by continuous co-localization of cellular extensions that span multiple channels and z-sections (Figure 1). Note that, the continuity is in the eye of the expert while myelin appears granular in digitized images due to the nature of staining. This necessitates the annotation to be pixel-based and th expert to fill the gaps making the process very laborious. In our workflow, co-localizing pixels, candidate myelins, were determined using Computer-assisted Evaluation of Myelin (CEM) software that we previously developed6. In this context, CEM software functions as a candidate myelin detection program because it simply identifies overlapping pixels. Briefly, CEM removes cell bodies, defined as the overlap of nuclei and cellular marker, and identifies overlapping pixels between remaining oligodendrocyte and neuron channels6.

In the current study, the CEMotate tool7 was developed to efficiently evaluate these candidate myelins and to extract myelin ground truths. Using CEMotate, an RGB-composite z-section image, corresponding CEM output image, and expert’s markings can be visualized simultaneously to decide whether to keep or remove candidate pixels (see Implementation). The user can move along x-y-z axes and show/hide channels, images, and markings. Markings from the -1/+1 z-sections can be viewed simultaneously. Finally, CEMotate enables two experts to independently mark myelin at different times and on different computers. When the files containing their myelin markings are shared and overlaid, it allows for the simultaneous visualization of both experts' annotations. This feature is crucial for inter-expert comparisons.

2c4e33a4-2cba-4de6-94db-3bf514957d0b_figure1.gif

Figure 1. An example of multi-spectral fluorescent image.

20× confocal microscopy image tiles were stitched together covering approximately 2 × 8 mm by 30–50 μm volume. Boxed area is enlarged to show myelin (brackets) and the false positive pixels (circles).

Using the described workflow, we annotated five images encompassing approximately 2 × 8 mm by 30–50 μm volume. The entire process, which would have taken several weeks, took approximately 5 days. More than 30,000 feature images were extracted from these five images and were used for testing various machine-learning methods810. The annotated images, which are available with the manuscript, are a resource for the researchers working not only on myelin detection but also on segmenting multi-spectral images.

Methods

Image acquisition

Images were previously acquired6. Briefly, co-cultures of mouse embryonic stem cell-derived oligodendrocytes and neurons were grown in microfluidic chambers. After myelin formation, cells were fixed in paraformaldehyde and were stained with 1:1,000 mouse or rabbit anti-TUJ1 (Covance), 1:50 rat anti-MBP (Serotec), and DAPI (Sigma). Images were acquired on Zeiss confocal microscopes as tiles approximately 2mm×8mm. The z-axis, 30–50 µm, was covered by 1-µm-thick optical z-sections. The tiles were stitched together on Zen software (Zeiss). No further processing was done.

Implementation

In CEMotate, a new project is started by loading oligodendrocyte, axon, and nucleus images, red, green, and blue channels respectively in the example (Figure 2). Users can save and reopen projects. In CEMotate, users can zoom using the mouse wheel and can move in the x-y axes and z-axis using scroll bars and buttons respectively (Figure 2 and Figure 3).

2c4e33a4-2cba-4de6-94db-3bf514957d0b_figure2.gif

Figure 2. Starting a new project in CEMotate.

Buttons for loading oligodendrocyte, axon, and nucleus images, and navigating the z-stack button to up and down are marked.

2c4e33a4-2cba-4de6-94db-3bf514957d0b_figure3.gif

Figure 3. Myelin drawing and saving in CEMotate.

The relevant buttons and myelin vectors are marked.

Myelin pixels may be marked at various thickness values (Figure 3). CEMotate records myelin drawings as vectors in the “.iev” files. These vectors can be modified or deleted in CEMotate (Figure 3). Optionally, to facilitate myelin detection, the candidate myelins can be loaded from CEM6 or another source that generates binary images of myelin markings. Myelin identification using CEM is described in detail in 6. Output of CEM, is a binary image, which is converted to vectors using the included module (Figure 4). Note that the conversion overwrites existing myelin vectors.

2c4e33a4-2cba-4de6-94db-3bf514957d0b_figure4.gif

Figure 4. Loading CEM output image.

To load candidate myelin pixels, use “Convert Binary Image to Vector” button.

Additionally, myelin regions from two sources can be visualized simultaneously. This allows visualization of myelins annotated by experts and CEM, to do so, first, rename and copy the ‘‘.iev’’ file containing second myelin vectors to the same folder. Next, modify the ‘‘.ini” files as shown in Figure 5. After loading the modified ‘‘.ini” file using the ‘Merge Edit’ button, myelin vectors will be shown in two different colors (Figure 6). These vectors can be modified as in Figure 6.

2c4e33a4-2cba-4de6-94db-3bf514957d0b_figure5.gif

Figure 5. Visualizing two myelin vectors simultaneously.

Modify .ini file as in the lower panels and load it using “Merge Edit” button.

2c4e33a4-2cba-4de6-94db-3bf514957d0b_figure6.gif

Figure 6. Modifying the myelin vectors.

CEM candidate myelins or two experts’ markings can be shortened, deleted or drawn over.

Once done with marking, users can convert the myelin vectors into an image using the “Save Myelin Mask Image” button. We implemented this strategy to extract gold standard myelin ground truths.

Comparative analysis

The myelins marked by two experts were compared against the gold standards. Experts’ precision for each image was calculated as described in 9. The average precision was calculated as mean of precision values of each expert for each image.

Operation

CEMotate is written in Pascal with the Delphi XE5 platform. The program can be run on 64-bit Microsoft Windows operating systems.

Results

In this study, myelin were marked by one moderately qualified and one entry level experts, on previously acquired oligodendrocyte and neuron co-culture images6 using the described workflow (see Implementation). A third, highly qualified expert evaluated their markings and extracted gold standard myelin ground truths. The ground truth images were saved as TIF on CEMotate7. All images are available (see below).

While CEM determined the candidate myelins on five images in approximately 43 minutes, ML approach took only 1.04 seconds for the same process8 (Table 1). Extracting the gold standard myelin ground truths from five images with candidate pixels that were determined by CEM took approximately another 35 hours for one expert. This process involved determining FPs and FNs on ImageJ. The same process took approximately 20 hours for an expert using CEMotate. Thus, over 40% of time was saved (Table 2). Moreover, CEMotate enabled collaboration of three experts for accelerated myelin ground truth extraction. Because ImageJ does not have such a feature, we could not directly compare the times saved for this process.

Table 1. Time comparison to detect myelin in five images for CEM and ML Approach.

CEMML Approach9
Time (~)43 min1.04 sec

Table 2. Time comparison for ImageJ and CEMotate annotation.

ImageJCEMotate
Time (~)35 hours20 hours

CEM identified 219032 candidate myelin pixels on five images. Two experts identified TP myelins. A third expert evaluated these results to obtain the gold standard myelin ground truths which covered 9550 pixels. To the best of our knowledge, this is the first time myelin ground truths of fluorescent images are shared with the science community.

Next, we calculated each expert's performance (Table 3). Two experts averaged 48.39% precision. The highest precision of an expert was 87.95% for one image. In comparison, our customized-CNN and Boosted Trees approaches, which were trained using ground truths images using the data annotated with CEM consistently reached precision values over 99%8. These results suggest that, machine learning methods can outperform human annotators once trained with accurately labeled data.

Table 3. Experts’ average precisions on candidate myelin pixels of five images.

Expert 1Expert 2
Average Precisions36.23%60.54%

Conclusion

CEMotate7 accelerates annotation of multi-spectral images. As an example, we used it to annotate myelin, which can only be identified as co-localization of neuron and oligodendrocyte membranes within certain criteria. CEMotate’s visualization features simplified inter-expert collaboration and validation. Moreover, myelin ground truths accompanying this manuscript are a resource for the researchers working on segmenting myelin and other features in multi-spectral images.

Comments on this article Comments (0)

Version 4
VERSION 4 PUBLISHED 21 Dec 2020
Comment
Author details Author details
Competing interests
Grant information
Copyright
Download
 
Export To
metrics
Views Downloads
F1000Research - -
PubMed Central
Data from PMC are received and updated monthly.
- -
Citations
CITE
how to cite this article
Çapar A, Çimen S, Aladağ Z et al. A multi-spectral myelin annotation tool for machine learning based myelin quantification [version 4; peer review: 2 approved]. F1000Research 2023, 9:1492 (https://doi.org/10.12688/f1000research.27139.4)
NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.
track
receive updates on this article
Track an article to receive email alerts on any updates to this article.

Open Peer Review

Current Reviewer Status: ?
Key to Reviewer Statuses VIEW
ApprovedThe paper is scientifically sound in its current form and only minor, if any, improvements are suggested
Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.
Not approvedFundamental flaws in the paper seriously undermine the findings and conclusions
Version 4
VERSION 4
PUBLISHED 15 Nov 2023
Revised
Views
6
Cite
Reviewer Report 20 Nov 2023
Mustafa Ozuysal, Department of Computer Engineering, Izmir Institute of Technology, Urla, Turkey 
Approved
VIEWS 6
The revision addresses ... Continue reading
CITE
CITE
HOW TO CITE THIS REPORT
Ozuysal M. Reviewer Report For: A multi-spectral myelin annotation tool for machine learning based myelin quantification [version 4; peer review: 2 approved]. F1000Research 2023, 9:1492 (https://doi.org/10.5256/f1000research.158759.r223202)
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
Version 3
VERSION 3
PUBLISHED 27 Apr 2022
Revised
Views
22
Cite
Reviewer Report 09 Nov 2022
Mustafa Ozuysal, Department of Computer Engineering, Izmir Institute of Technology, Urla, Turkey 
Approved with Reservations
VIEWS 22
The manuscript describes an annotation tool for myelin sheets in stacks of fluorescent images and a novel data set annotated using this tool. Annotation performance using the proposed tool is compared to existing software.

Overall, the article ... Continue reading
CITE
CITE
HOW TO CITE THIS REPORT
Ozuysal M. Reviewer Report For: A multi-spectral myelin annotation tool for machine learning based myelin quantification [version 4; peer review: 2 approved]. F1000Research 2023, 9:1492 (https://doi.org/10.5256/f1000research.133487.r154267)
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
Views
16
Cite
Reviewer Report 27 Apr 2022
Predrag Janjic, Research Center for Computer Science and Information Technology, Macedonian Academy of Sciences and Arts, Skopje, North Macedonia 
Approved
VIEWS 16
It is acknowledged that the ... Continue reading
CITE
CITE
HOW TO CITE THIS REPORT
Janjic P. Reviewer Report For: A multi-spectral myelin annotation tool for machine learning based myelin quantification [version 4; peer review: 2 approved]. F1000Research 2023, 9:1492 (https://doi.org/10.5256/f1000research.133487.r136045)
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
Version 2
VERSION 2
PUBLISHED 09 Mar 2022
Revised
Views
22
Cite
Reviewer Report 08 Apr 2022
Predrag Janjic, Research Center for Computer Science and Information Technology, Macedonian Academy of Sciences and Arts, Skopje, North Macedonia 
Approved
VIEWS 22
The authors have addressed the issues within the initial review carefully.

I would suggest that the introduction stresses clearly that the specific nature of myelin identification and annotation difficulties this work addresses matter specifically for fluorescent imaging ... Continue reading
CITE
CITE
HOW TO CITE THIS REPORT
Janjic P. Reviewer Report For: A multi-spectral myelin annotation tool for machine learning based myelin quantification [version 4; peer review: 2 approved]. F1000Research 2023, 9:1492 (https://doi.org/10.5256/f1000research.121217.r126756)
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
  • Author Response 27 Apr 2022
    Bilal Kerman, Regenerative and Restorative Medicine Research Center, Istanbul Medipol University, Istanbul, 34810, Turkey
    27 Apr 2022
    Author Response
    Dear Dr. Janjic,

    Thank you very much for your approval and again noticing an important detail. We updated the manuscript to emphasize that this tool is for fluorescent images. Analysis ... Continue reading
COMMENTS ON THIS REPORT
  • Author Response 27 Apr 2022
    Bilal Kerman, Regenerative and Restorative Medicine Research Center, Istanbul Medipol University, Istanbul, 34810, Turkey
    27 Apr 2022
    Author Response
    Dear Dr. Janjic,

    Thank you very much for your approval and again noticing an important detail. We updated the manuscript to emphasize that this tool is for fluorescent images. Analysis ... Continue reading
Version 1
VERSION 1
PUBLISHED 21 Dec 2020
Views
49
Cite
Reviewer Report 08 Feb 2021
Predrag Janjic, Research Center for Computer Science and Information Technology, Macedonian Academy of Sciences and Arts, Skopje, North Macedonia 
Not Approved
VIEWS 49
The manuscript introduces a 3D extension of myelin annotation tool reported in Ref[5], now applicable to fluorescent stacks. Although the main rationale to develop an automated tool for producing ground truth images is clear, and the importance has been laid ... Continue reading
CITE
CITE
HOW TO CITE THIS REPORT
Janjic P. Reviewer Report For: A multi-spectral myelin annotation tool for machine learning based myelin quantification [version 4; peer review: 2 approved]. F1000Research 2023, 9:1492 (https://doi.org/10.5256/f1000research.29981.r76521)
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
  • Author Response 09 Mar 2022
    Bilal Kerman, Regenerative and Restorative Medicine Research Center, Istanbul Medipol University, Istanbul, 34810, Turkey
    09 Mar 2022
    Author Response
    We thank Predrag Janjic for his helpful comments. We believe that we address his concerns and the updated manuscript is easier to read and more satisfactory to the readers. Please ... Continue reading
COMMENTS ON THIS REPORT
  • Author Response 09 Mar 2022
    Bilal Kerman, Regenerative and Restorative Medicine Research Center, Istanbul Medipol University, Istanbul, 34810, Turkey
    09 Mar 2022
    Author Response
    We thank Predrag Janjic for his helpful comments. We believe that we address his concerns and the updated manuscript is easier to read and more satisfactory to the readers. Please ... Continue reading

Comments on this article Comments (0)

Version 4
VERSION 4 PUBLISHED 21 Dec 2020
Comment
Alongside their report, reviewers assign a status to the article:
Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested
Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.
Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions
Sign In
If you've forgotten your password, please enter your email address below and we'll send you instructions on how to reset your password.

The email address should be the one you originally registered with F1000.

Email address not valid, please try again

You registered with F1000 via Google, so we cannot reset your password.

To sign in, please click here.

If you still need help with your Google account password, please click here.

You registered with F1000 via Facebook, so we cannot reset your password.

To sign in, please click here.

If you still need help with your Facebook account password, please click here.

Code not correct, please try again
Email us for further assistance.
Server error, please try again.