Myelin detection in fluorescence microscopy images using machine learning

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

doi:10.1016/j.jneumeth.2020.108946

Cited by 7 publications

(3 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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 8 . These results suggest that, machine learning methods can outperform human annotators once trained with accurately labeled data.…”

Section: Resultsmentioning

confidence: 89%

“…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%

See 1 more Smart Citation

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

et al. 2020

Self Cite

View full text Add to dashboard Cite

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

Section: Resultsmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Regarding traditional ML approaches, our bibliographic search resulted in only a few relevant publications indicating that this AI application field is still understudied. Particularly, in [10] various machine learning techniques were evaluated to accurately detect myelin in multi-channel microscopy images of a mouse stem cell. Another study presents the application of machine learning (classification pipeline) for the real time visualization of tumor margins in excised breast specimens using fluorescence lifetime imaging [11].…”

Section: Related Workmentioning

confidence: 99%

Pathomics and Deep Learning Classification of a Heterogeneous Fluorescence Histology Image Dataset

et al. 2021

View full text Add to dashboard Cite

Automated pathology image classification through modern machine learning (ML) techniques in quantitative microscopy is an emerging AI application area aiming to alleviate the increased workload of pathologists and improve diagnostic accuracy and consistency. However, there are very few efforts focusing on fluorescence histology image data, which is a challenging task, not least due to the variable imaging acquisition parameters in pooled data, which can diminish the performance of ML-based decision support tools. To this end, this study introduces a harmonization preprocessing protocol for image classification within a heterogeneous fluorescence dataset in terms of image acquisition parameters and presents two state-of-the-art feature-based approaches for differentiating three classes of nuclei labelled by an expert based on (a) pathomics analysis scoring an accuracy (ACC) up to 0.957 ± 0.105, and, (b) transfer learning model exhibiting ACC up-to 0.951 ± 0.05. The proposed analysis pipelines offer good differentiation performance in the examined fluorescence histology image dataset despite the heterogeneity due to the lack of a standardized image acquisition protocol.

show abstract

A comparative study on machine learning-based classification to find photothrombotic lesion in histological rabbit brain images

Kim

Lee

et al. 2021

J. Innov. Opt. Health Sci.

View full text Add to dashboard Cite

Recently, research has been conducted to assist in the processing and analysis of histopathological images using machine learning algorithms. In this study, we established machine learning-based algorithms to detect photothrombotic lesions in histological images of photothrombosis-induced rabbit brains. Six machine learning-based algorithms for binary classification were applied, and the accuracies were compared to classify normal tissues and photothrombotic lesions. The lesion classification model consisting of a 3-layered neural network with a rectified linear unit (ReLU) activation function, Xavier initialization, and Adam optimization using datasets with a unit size of [Formula: see text] pixels yielded the highest accuracy (0.975). In the validation using the tested histological images, it was confirmed that the model could identify regions where brain damage occurred due to photochemical ischemic stroke. Through the development of machine learning-based photothrombotic lesion classification models and performance comparisons, we confirmed that machine learning algorithms have the potential to be utilized in histopathology and various medical diagnostic techniques.

show abstract

Myelin detection in fluorescence microscopy images using machine learning

Cited by 7 publications

References 25 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

Pathomics and Deep Learning Classification of a Heterogeneous Fluorescence Histology Image Dataset

A comparative study on machine learning-based classification to find photothrombotic lesion in histological rabbit brain images

Contact Info

Product

Resources

About