DeepMQ: A Deep Learning Approach Based Myelin Quantification in Microscopic Fluorescence Images

Cimen, Sibel; Çapar, Abdulkerim; Ekinci, Dursun Ali; Ayten, Umut Engin; Kerman, Bilal E.; Töreyi̇n, Behçet Uğur

doi:10.23919/eusipco.2018.8553438

Cited by 5 publications

(5 citation statements)

References 16 publications

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“…The highest precision of an expert was 87.95% for one image. In comparison, our customized-CNN and Boosted Trees consistently reached precision values over 99% 8 . These results suggest that, machine learning methods can outperform human annotators once trained with accurately labeled data.…”

Section: Resultsmentioning

confidence: 88%

“…Therefore, we believe that Fig. 1 Comparative analysis: Some more data is needed, new or from Ref [8] for a reader to be able to get the overall impression. Please consider adding a Benchmarking paragraph where you would extend a bit on some benchmarking given within the Results, with an estimate (table) of the computational time needed for the CEM scope per slice, and the total time to process a whole stack, all in order a potential end user to get an impression of the effort needed.…”

Section: Image Acquisitionmentioning

confidence: 99%

“…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 methods [8][9][10] . 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.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Resultsmentioning

confidence: 88%

Section: Image Acquisitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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“…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 methods [7][8][9] . 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.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

Self Cite

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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 necessitates expert labor. To facilitate myelin annotation, we developed a workflow and a software for myelin ground truth extraction from multi-spectral fluorescent images. Additionally, we shared a set of myelin ground truths annotated using this workflow.

show abstract

“…Comparative analysis: Some more data is needed, new or from Ref [8] for a reader to be able to get the overall impression. Please consider adding a Benchmarking paragraph where you would extend a bit on some benchmarking given within the Results, with an estimate (table) of the computational time needed for the CEM scope per slice, and the total time to process a whole stack, all in order a potential end user to get an impression of the effort needed.…”

mentioning

confidence: 99%

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

et al. 2022

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DeepMQ: A Deep Learning Approach Based Myelin Quantification in Microscopic Fluorescence Images

Cited by 5 publications

References 16 publications

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

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

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

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

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