Identification and segmentation of myelinated nerve fibers in a cross-sectional optical microscopic image using a deep learning model

Naito, Tatsuhiko; Nagashima, Yu; Taira, Kenichiro; Uchio, Naohiro; Tsuji, Shoji; Shimizu, Jun

doi:10.1016/j.jneumeth.2017.08.014

Cited by 23 publications

(24 citation statements)

References 20 publications

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“…With our large dataset, this resulted in statistically significant differences even though the absolute differences were small. The absolute values of these parameters are known to differ between research labs due to differences in staining and imaging protocols [5,6], and the judgment of the individual raters doing the segmentations. Therefore, consistency within studies is most important, and ADS was able to provide reliable measurements with comparable variability as manual analysis.…”

Section: Discussionmentioning

confidence: 99%

“…Besides leveraging deep learning, ADS has a number of advantages over other programs that have been developed for axon histomorphometry [5][6][7]17]. ADS is open source and freely available for download from GitHub (https://github.com/neuropoly/axondeepseg).…”

Section: Discussionmentioning

confidence: 99%

“…These widely recognized limitations of manual AH have inspired many tools to achieve some degree of automation [3][4][5]. A major obstacle to full automation using traditional programming methods is the difficulty of achieving axonal segmentation; that is, the process by which the boundaries of the myelin sheath surrounding individual axons are defined so that each axon can be measured separately [3,6,7] To illustrate the challenge of axonal segmentation, axons in undamaged nerves reside in close proximity with myelin sheaths tending to abut each other; in comparison, debris is frequently observed in regenerating nerves that can be mislabeled as an axon. These and other nuances need to be carefully defined to improve axonal segmentation, but manual correction is still required due to frequent errors that can affect downstream calculations and substantially reduce measurement accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Deep learning, also known as computer vision, is a subset of machine learning that can use convolutional neural networks, where each network hierarchically defines specific features of images and does not require structured numerical input data [9,10]. Various programs have leveraged deep learning for nerve analysis, but their generalizability has not been investigated for implementation outside of the research groups in which they were developed [4][5][6][7][10][11][12]. AxonDeepSeg (ADS) is a novel deep learning program for fully-automated AH originally developed using both scanning-and transmission-electron micrographs of the brain and spinal cord [7].…”

Section: Introductionmentioning

confidence: 99%

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A simple and robust method for automating analysis of naïve and regenerating peripheral nerves

Wong

Hricz

Malapati

et al. 2021

Preprint

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Background Manual axon histomorphometry (AH) is time- and resource-intensive, which has inspired many attempts at automation. However, there has been little investigation on implementation of automated programs for widespread use. Ideally such a program should be able to perform AH across imaging modalities and nerve states. AxonDeepSeg (ADS) is an open source deep learning program that has previously been validated in electron microscopy. We evaluated the robustness of ADS for peripheral nerve axonal histomorphometry in light micrographs prepared using two different methods. Methods Axon histomorphometry using ADS and manual analysis (gold-standard) was performed on light micrographs of naïve or regenerating rat median nerve cross-sections prepared with either toluidine-resin or osmium-paraffin embedding protocols. The parameters of interest included axon count, axon diameter, myelin thickness, and g-ratio. Results Manual and automatic ADS axon counts demonstrated good agreement in naïve nerves and moderate agreement on regenerating nerves. There were small but consistent differences in measured axon diameter, myelin thickness and g-ratio; however, absolute differences were small. Both methods appropriately identified differences between naïve and regenerating nerves. ADS was faster than manual axon analysis. Conclusions Without any algorithm retraining, ADS was able to appropriately identify critical differences between naïve and regenerating nerves and work with different sample preparation methods of peripheral nerve light micrographs. While there were differences between absolute values between manual and ADS, ADS performed consistently and required much less time. ADS is an accessible and robust tool for AH that can provide consistent analysis across protocols and nerve states.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A simple and robust method for automating analysis of naïve and regenerating peripheral nerves

Wong

Hricz

Malapati

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…These models have achieved record-breaking results in various fields, largely due to the recent revival of convolutional neural networks. Their performance is remarkable especially in image processing (Krizhevsky, Sutskever, & Hinton, 2012), and they have been applied to automated image recognition systems such as pathological images with a high level of accuracy (Naito et al, 2017). In addition, there are several successful examples of the application of DNN to establish a predictive system for determining the effects of genetic variants, and DNN was also used in the CAGI challenges (Laksshman, Bhat, Viswanath, & Li, 2017).…”

Section: Deep Neural Networkmentioning

confidence: 99%

Predicting the impact of single nucleotide variants on splicing via sequence‐based deep neural networks and genomic features

Naito

2019

Human Mutation

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Single nucleotide mutations in exonic regions can significantly affect gene function through a disruption of splicing, and various computational methods have been developed to predict the splicing‐related effects of a single nucleotide mutation. We implemented a new method using ensemble learning that combines two types of predictive models: (a) base sequence‐based deep neural networks (DNNs) and (b) machine learning models based on genomic attributes. This method was applied to the Massively Parallel Splicing Assay challenge of the Fifth Critical Assessment of Genome Interpretation, in which challenge participants predicted various experimentally‐defined exonic splicing mutations, and achieved a promising result. We successfully revealed that combining different predictive models based upon the stacked generalization method led to significant improvement in prediction performance. In addition, whereas most of the genomic features adopted in constructing machine learning models were previously reported, feature values generated with DSSP, a DNN‐based splice site prediction tool, were novel and helpful for the prediction. Learning the sequence patterns associated with normal splicing and the change in splicing site probabilities caused by a mutation was presumed to be helpful in predicting splicing disruption.

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Robust and Markerfree in vitro Axon Segmentation with CNNs

Grüning

Palumbo

Landt

et al. 2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Identification and segmentation of myelinated nerve fibers in a cross-sectional optical microscopic image using a deep learning model

Cited by 23 publications

References 20 publications

A simple and robust method for automating analysis of naïve and regenerating peripheral nerves

A simple and robust method for automating analysis of naïve and regenerating peripheral nerves

Predicting the impact of single nucleotide variants on splicing via sequence‐based deep neural networks and genomic features

Robust and Markerfree in vitro Axon Segmentation with CNNs

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