ArticleTranslational vision science & technology2021
AxonDeep: Automated Optic Nerve Axon Segmentation in Mice With Deep Learning.
Article in Translational vision science & technology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- Deep learning and optical coherence tomography in glaucoma: Bridging the diagnostic gap on structural imaging.Frontiers in ophthalmology · 2022Pooled it
- BasNet: Attention U-Net-Based Automated Axon Segmentation in Bielschowsky Silver-Stained Histology.Neuroinformatics · 2026Article
- Comparison of Deep Learning Tools for Optic Nerve Axon Quantification Finds Limited Generalizability Upon Independent Validation.Bioengineering (Basel, Switzerland) · 2026Article
- Comparison of Deep Learning Tools for Optic Nerve Axon Quantification Finds Limited Generalizability on Independent Validation.bioRxiv : the preprint server for biology · 2026Article
- AxoMetric platform facilitates rapid and unbiased automated quantification of axon regeneration in tissue sections.Cell reports methods · 2026Article
- Mitochondrially Targeted Gene Therapy Rescues Visual Loss in a Mouse Model of Leber's Hereditary Optic Neuropathy.International journal of molecular sciences · 2023Article
- AxonQuantifier: A semi-automated program for quantifying axonal density from whole-mounted optic nerves.Journal of neuroscience methods · 2023Article
- [A region-level contrastive learning-based deep model for glomerular ultrastructure segmentation on electron microscope images].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2023Article
- RGC-Net: An Automatic Reconstruction and Quantification Algorithm for Retinal Ganglion Cells Based on Deep Learning.Translational vision science & technology · 2023Article
- AxoNet 2.0: A Deep Learning-Based Tool for Morphometric Analysis of Retinal Ganglion Cell Axons.Translational vision science & technology · 2023Article
- Reverse translation of artificial intelligence in glaucoma: Connecting basic science with clinical applications.Frontiers in ophthalmology · 2023Article
- Automated pipeline for nerve fiber selection and g-ratio calculation in optical microscopy: exploring staining protocol variations.Frontiers in neuroanatomy · 2023Article
- Biological Correlations and Confounders for Quantification of Retinal Ganglion Cells by Optical Coherence Tomography Based on Studies of Outbred Mice.Translational vision science & technology · 2022Article
- Rapid, automated nerve histomorphometry through open-source artificial intelligence.Scientific reports · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
Purpose: Optic nerve damage is the principal feature of glaucoma and contributes to vision loss in many diseases. In animal models, nerve health has traditionally been assessed by human experts that grade damage qualitatively or manually quantify axons from sampling limited areas from histologic cross sections of nerve. Both approaches are prone to variability and are time consuming. First-generation automated approaches have begun to emerge, but all have significant shortcomings. Here, we seek improvements through use of deep-learning approaches for segmenting and quantifying axons from cross-sections of mouse optic nerve. Methods: Two deep-learning approaches were developed and evaluated: (1) a traditional supervised approach using a fully convolutional network trained with only labeled data and (2) a semisupervised approach trained with both labeled and unlabeled data using a generative-adversarial-network framework. Results: From comparisons with an independent test set of images with manually marked axon centers and boundaries, both deep-learning approaches outperformed an existing baseline automated approach and similarly to two independent experts. Performance of the semisupervised approach was superior and implemented into AxonDeep. Conclusions: AxonDeep performs automated quantification and segmentation of axons from healthy-appearing nerves and those with mild to moderate degrees of damage, similar to that of experts without the variability and constraints associated with manual performance. Translational Relevance: Use of deep learning for axon quantification provides rapid, objective, and higher throughput analysis of optic nerve that would otherwise not be possible.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.