Biology subjects

Chuter, B.

Publications and source records attributed to Chuter, B..

5 recordsLinked to original sources

bioRxiv2026

BXD51: A Robust and Translational Mouse Model for Studying the Pathophysiology of Glaucoma

The aim of this study was to characterize the BXD51 mouse strain as a reproducible model of chronic progressive glaucoma. Unlike the highly susceptible DBA/2J (D2) mutant strain, BXD51 is a genetically stable recombinant inbred line derived from C57BL/6J (B6) and D2 parental lines. Longitudinal assessments of intraocular pressure (IOP), visual acuity (VA), contrast sensitivity (CS), and pattern electroretinogram (pERG) demonstrated that BXD51 mice undergo a delayed decline in visual and retinal ganglion cell (RGC) function. Their decline is biphasic, with a period of initial ocular stress followed by a late-onset, accelerated structural and functional deterioration of RGCs. Anterior segment structural analysis by optical coherence tomography (OCT) and histology demonstrated increasing pigment dispersion and subsequent iridocorneal angle closure. Immunofluorescence analysis of structural neuronal markers (TUBB3 and MAP1A/2) exhibited thinning of the ganglion cell layer (GCL) and inner plexiform layer (IPL) together with axonal degeneration, mirroring the laminar degeneration seen in human glaucoma patients. BXD51 also revealed marked spatial heterogeneity between peripheral and central retina. Multivariate analysis confirmed that BXD51 follows a distinct clinical trajectory that separates it from both wild-type (B6) and a severe glaucoma model (D2). By spanning the range between resistance and extreme susceptibility to glaucomatous neurodegeneration, this study establishes the BXD51 mouse as a translational platform for mechanistic studies and for evaluating long-term neuroprotective strategies.

neuroscience↗

bioRxiv2026

A Systematic Review and Independent Benchmarking of Automated Nerve Morphometry Methods

Objective: To systematically review automated nerve morphometry tools and independently benchmark their performance on independent optic nerve datasets. Design: Systematic review and comparative benchmarking study. Controls: Benchmarking was performed using paraphenylenediamine-stained mouse (n = 85) and rat (n = 44) optic nerve images with manually annotated axon counts as ground truth. Methods: Published studies describing automated or semi-automated neural tissue morphometry tools were identified through systematic searches of PubMed, Embase, and Scopus through January 2026 following PRISMA guidelines. Data extraction covered 70 fields across tool capabilities, imaging modality, species, automation level, and validation approach. Eighteen eligible tools (8 deep learning [DL], 10 classical computer vision [CV]) were benchmarked on both mouse and rat independent datasets. Main Outcome Measures: Performance was assessed by mean absolute percentage error (MAPE), Pearson correlation, and median predicted-to-ground-truth ratio. Tools were ranked per image and compared using Friedman tests with Nemenyi post-hoc analysis. Results: Seventy-one studies met inclusion criteria, spanning from 1999 to 2026. Deep learning methods represented 38% (27/71) of studies, increasing from 0% before 2017 to over 55% of publications after 2020. Axon counting was the most common output (73%, 52/71), while only 35% (25/71) reported g-ratio. Among benchmarked tools, Marina (CV, 2010) achieved the lowest average MAPE (32.9%). The top five tools (MAPE ranging from 32.9 to 44.8%) included both CV and DL methods and were statistically indistinguishable by Friedman-Nemenyi analysis (p > 0.05). Performance varied substantially across datasets: AxonJ (CV) achieved the second best MAPE on rat images (27.7%) but the worst on mouse images (438.6%). Conclusions: No single tool demonstrated consistently superior performance across both datasets. Classical and deep learning approaches achieved comparable accuracy for axon counting. Tool selection should be guided by target species, tissue preparation protocol, and desired morphometric outputs. This systematic review and independent benchmarking study provide an evidence base for tool selection in optic nerve research.

bioengineering↗

bioRxiv2026

Visual Function Correlates More Strongly with Glial Coverage than Axon Count Across Multiple Mouse Strains

ObjectiveTo determine whether non-axon optic nerve morphometric features correlate with clinical visual function as strongly as the traditional axon count gold standard. DesignCross-sectional histological analysis with longitudinal clinical correlation. SubjectsEighteen mice from three strains: C57BL/6J (n=6), BXD51 (n=6), and DBA/2J (n=6). MethodsLeft eye (OS) optic nerves from mice euthanized at 12 months of age were resin-embedded and stained with p-phenylenediamine. Bright-field cross-sectional images were segmented using an AxonDeepSeg-based workflow to generate axon, myelin, whole nerve, and glial coverage masks for morphometric quantification. Seven morphometrics were extracted: axon count (nAx), axon density (AxDen), glial coverage area ratio (GliaR), mean solidity (Sol), mean axon diameter (AxDiam), mean myelin area (MyArea), and mean axon-myelin area (AxMyArea). Morphometrics were correlated with longitudinal clinical data collected at 1, 3, 6, 9, and 12 months, including visual acuity (VA), contrast threshold, intraocular pressure (IOP), and pattern electroretinography P50 and N95 amplitudes (PERG P50 and N95). Main Outcome MeasuresPearson correlation coefficients were used to assess associations between morphometric features and clinical measures, and Fisher z-transformed meta-analytic correlations were used to aggregate these associations across ages. ResultsVA and contrast threshold demonstrated strong correlations with GliaR that matched or exceeded nAx. Meta-analysis across ages revealed GliaR correlated with VA (r = -0.84, p = 4.49 x 10-21) and contrast threshold (r = 0.86, p=7.55 x 10-23), comparable to nAx correlations with VA (r = 0.80, p=8.13x10-17) and contrast threshold (r = -0.80, p= 1.74x10-16). Structure-function relationships shifted with age: at 6 months, GliaR had the strongest correlation with contrast threshold (r = 0.96), while at 12 months, AxDiam became the dominant correlate of both VA (r = 0.77) and contrast threshhold (r = -0.74). IOP, PERG P50, and PERG N95 exhibited weak correlations with all morphometrics (|r| < 0.27). ConclusionsNon-axon morphometrics, particularly glial coverage area ratio, correlate with visual function as strongly as traditional axon count. Automated optic nerve assessment should incorporate glial and other non-axon features. Further, stage-aware biomarker selection may better capture structure-function relationships in glaucoma.

neuroscience↗

bioRxiv2026

Comparison of Deep Learning Tools for Optic Nerve Axon Quantification Finds Limited Generalizability on Independent Validation

PurposeMachine learning approaches for automated quantification of optic nerve histology have emerged as potential tools for objective assessment of axonal injury in experimental glaucoma models. However, the generalizability of these models to independent datasets remains unclear. Guided by a scoping review of the literature, this study performed independent validation testing of publicly available models on a novel rat optic nerve dataset to assess their generalizability. MethodsWe conducted a scoping review following PRISMA-ScR guidelines. PubMed, EMBASE, Scopus, and Cochrane CENTRAL were searched from 2000 through 2025. Two reviewers independently screened records and extracted data on model characteristics and performance metrics. Additionally, we performed independent validation of three models (AxoNet, AxonDeepSeg, AxoNet 2.0) on a novel rat optic nerve dataset comprising 57 images with 9,514 manually annotated axons. Because AxonDeep is not publicly available, we instead evaluated AxonDeepSeg, a separate publicly available deep learning-based tool that, while not previously applied to optic nerve tissue, is widely used for nerve fiber segmentation. ResultsFrom 2,036 records, four manuscripts describing three deep learning models met inclusion criteria. Published correlation coefficients between model predictions and reference counts ranged from 0.959 to 0.99. On independent validation, performance was reduced: AxoNet 2.0 achieved the highest correlation (r = 0.89), followed by AxonDeepSeg (r = 0.86) and AxoNet (r = 0.79). Segmentation quality metrics revealed high precision (>0.94) but low recall (0.18 to 0.27), with Dice coefficients of 0.29 to 0.40, substantially below published benchmarks of 0.81. ConclusionsDeep learning models for optic nerve histology demonstrate strong within-study performance but show meaningful performance decrements when applied to independent datasets. The observed generalizability gap (correlations 0.07 to 0.182 points below published values) demonstrates the need for standardized validation datasets and multi-center testing before widespread adoption of these tools.

bioengineering↗

bioRxiv2026

MONICA: A Web Application for Automated Whole Optic Nerve Contour Extraction and Morphometric Analysis Validated Across Taxonomic Orders and Image Quality Levels

Quantitative assessment of optic nerve health requires metrics beyond axon counts alone. Axon density and glial coverage fraction correlate with clinical measures of visual function, yet no existing automated tool extracts optic nerve cross-sectional boundaries to enable their calculation. We developed MONICA (Morphometrics from Optic Nerve Imaging Contour Analysis), a web application that integrates AxonDeepSeg deep learning segmentation with a novel morphology-based contour extraction algorithm to automatically derive whole nerve boundaries alongside axon and myelin masks. The contour extraction algorithm was validated against manual ground truth annotations using 15 optic nerve cross-sections spanning two taxonomic orders (mouse, rabbit), two mouse strains (BXD29, BXD51), and varying preparation quality levels (modern and archival samples). Automated contour extraction demonstrated excellent agreement with manual annotations, achieving an overall Dice similarity coefficient (a measure of segmentation overlap) of 0.987 {+/-} 0.009. Balanced precision (0.985) and recall (0.989) values indicated that the algorithm neither systematically over-segments nor under-segments nerve boundaries. MONICA requires no local software installation and runs entirely in-browser, providing batch processing for high-throughput phenotyping alongside a full suite of per-axon morphometrics. MONICA provides researchers with an accessible tool for complete nerve cross-section morphometry.

neuroscience↗