Automatic identification and morphometry of optic nerve fibers in electron microscopy images

Zhao, Xiangmo; Pan, Zhenkuan; Wu, Jing; Zhou, Guomin; Zeng, Yanjun

doi:10.1016/j.compmedimag.2009.08.009

Cited by 22 publications

(24 citation statements)

References 16 publications

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“…The latter are very attractive since they are usually faster than semi-automated methods and are not user-dependent. While most of these methods are based on typical segmentation techniques such as template matching [12], edge detection [13][14][15], zonal graph [16], thresholding [17,18], neural networks [19] and region growing [20,21], other contributions rely on multiple stage methods using a combination of techniques: elliptical Hough transform followed by an active contour model [22], multi-level gradient watershed and fuzzy systems [23]. Li et al [24] use a classification algorithm (spectral angle mapper) to segment nerve fibers in hyperspectral images.…”

Section: Introductionmentioning

confidence: 99%

“…While the majority of these methods were developed for standard light microscopy images using staining such as toluidine blue or osmium tetroxide, some were intended to work with transmission electron microscope images [19,20] and scanning electron microscope images [11]. To the best of our knowledge, there are only a few studies whereby semi-automated or automated segmentation was developed for nonlinear optical microscopy images to find cell nuclei [25][26][27][28][29] and none to extract nerve fiber morphology.…”

Section: Introductionmentioning

confidence: 99%

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Automated method for the segmentation and morphometry of nerve fibers in large-scale CARS images of spinal cord tissue

Bégin

Dupont-Therrien

Bélanger

et al. 2014

Biomed. Opt. Express

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A fully automated method for large-scale segmentation of nerve fibers from coherent anti-Stokes Raman scattering (CARS) microscopy images is presented. The method is specifically designed for CARS images of transverse cross sections of nervous tissue but is also suitable for use with standard light microscopy images. After a detailed description of the two-part segmentation algorithm, its accuracy is quantified by comparing the resulting binary images to manually segmented images. We then demonstrate the ability of our method to retrieve morphological data from CARS images of nerve tissue. Finally, we present the segmentation of a large mosaic of CARS images covering more than half the area of a mouse spinal cord cross section and show evidence of clusters of neurons with similar g-ratios throughout the spinal cord.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated method for the segmentation and morphometry of nerve fibers in large-scale CARS images of spinal cord tissue

Bégin

Dupont-Therrien

Bélanger

et al. 2014

Biomed. Opt. Express

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“…Several segmentation methods for axon and myelin have been proposed which are based on traditional image processing algorithms including thresholding and morphological operations 8,9 , axon shape-based morphological discrimination 10 , watershed 11,12 , region growing 13 , active contours without 14,15 and with discriminant analysis 15 . However, few limitations can be reported from the previous work: (i) traditional image-based methods are designed to work on specific imaging modalities and often fail if another contrast is used (e.g., optical image instead of electron microscopy); (ii) previous methods are not fully-automatic as they typically require either preprocessing, hand-selected features for axon discrimination and/or postprocessing; (iii) traditional image-based methods do not make full use of the contextual information of the image (i.e., multi-scale representation of axons, average shape of axons, etc.)…”

Section: Introductionmentioning

confidence: 99%

AxonDeepSeg: automatic axon and myelin segmentation from microscopy data using convolutional neural networks

Zaimi

Wabartha

Herman

et al. 2018

Sci Rep

119

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Segmentation of axon and myelin from microscopy images of the nervous system provides useful quantitative information about the tissue microstructure, such as axon density and myelin thickness. This could be used for instance to document cell morphometry across species, or to validate novel non-invasive quantitative magnetic resonance imaging techniques. Most currently-available segmentation algorithms are based on standard image processing and usually require multiple processing steps and/or parameter tuning by the user to adapt to different modalities. Moreover, only a few methods are publicly available. We introduce AxonDeepSeg, an open-source software that performs axon and myelin segmentation of microscopic images using deep learning. AxonDeepSeg features: (i) a convolutional neural network architecture; (ii) an easy training procedure to generate new models based on manually-labelled data and (iii) two ready-to-use models trained from scanning electron microscopy (SEM) and transmission electron microscopy (TEM). Results show high pixel-wise accuracy across various species: 85% on rat SEM, 81% on human SEM, 95% on mice TEM and 84% on macaque TEM. Segmentation of a full rat spinal cord slice is computed and morphological metrics are extracted and compared against the literature. AxonDeepSeg is freely available at https://github.com/neuropoly/axondeepseg.

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“…However, numerous locations in the nervous system contain exceedingly large numbers of nerve fibres; for example, the rat optic nerve contains more than 100,000 fibres [17] and the human optic nerve more than a million [34] [35] [36]. In these cases, manual A c c e p t e d M a n u s c r i p t morphometry is very monotonous, tiring, time-consuming, and predisposed to error [37]. Hence, researchers have been adopting different analysis systems to study the morphological and morphometric features of nerve fibres [8] [36] [38] [39] in order to significantly reduce data input and processing times.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, researchers have been adopting different analysis systems to study the morphological and morphometric features of nerve fibres [8] [36] [38] [39] in order to significantly reduce data input and processing times. A variety of sampling schemes claim to be capable of resolving this problem and guarantee the reliability of morphometry [37]. Consequently, there is high motivation for for the development of automatic morphometry systems.…”

Section: Introductionmentioning

confidence: 99%

Identifying central and peripheral nerve fibres with an artificial intelligence approach

Gil

Girela

Molina‐Azorín

et al. 2018

Applied Soft Computing

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Distinguishing axons from central or peripheral nervous systems (CNS or PNS, respectively) is often a complicated task. The main objective of this work was to facilitate and support the process of automatically distinguishing the different types of nerve fibres by analysing their morphological characteristics. Our approach was based on a multi-level hierarchical classifier architecture that can handle the complexity of directly identifying nerve-fibre groups belonging to either the CNS or the PNS. The approach adopted comprises supervised methods (multilayer perceptron and decision trees), which are responsible for distinguishing the origin of the axons (CNS or PNS), whereas the unsupervised method (K-Means clustering) performs nerve fibre clustering based on similar characteristics for both the CNS and PNS. Our experiments produced results with an accuracy higher than 88%. Our findings suggest that the development and implementation of a multi-level system improves automation capabilities and increases accuracy in the classification of nerves. Furthermore, our architecture allows for generalisation and flexibility, which can subsequently be extended to other biological control systems.

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Automatic identification and morphometry of optic nerve fibers in electron microscopy images

Cited by 22 publications

References 16 publications

Automated method for the segmentation and morphometry of nerve fibers in large-scale CARS images of spinal cord tissue

Automated method for the segmentation and morphometry of nerve fibers in large-scale CARS images of spinal cord tissue

AxonDeepSeg: automatic axon and myelin segmentation from microscopy data using convolutional neural networks

Identifying central and peripheral nerve fibres with an artificial intelligence approach

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