Abstract
Accurate segmentation of axons in 3D microscopy data is important for analyzing white-matter organization, but dense ground truth labels are expensive to obtain. Existing supervised axon segmentation methods rely on target-domain annotations and can be brittle when tissue type, species, modality, or acquisition conditions change. WepresentAxonSynth,adomain-randomizedsynthetic-dataframework for training 3D axon segmentation models without manually annotated real training volumes. AxonSynth generates dense synthetic axon labels withorientationpriorsthatreflectrealisticfiberconfigurationsandrendersthemwithrandomizeddensity,contrast,biasfields,blur,andnoise. A three-class 3D U-Net is trained to predict background, axon sheath and intra-axonal space. We evaluate zero-shot transfer on 10 held-out light-sheet microscopy (LSM) patches from macaque and human brain samples labeled with one of three axonal markers, comparing against calibrated thresholding and Frangi filtering using overlap, corrected detection, false-positive, and topology metrics. On macaque samples, AxonSynthachievedthebestcorrectedDiceandcorrectedprecision(0.826 and 0.851), compared with 0.765 and 0.754 for thresholding and 0.685 and0.762forFrangi.Onhumansamples,correctedDicewascomparable tothresholding(0.857vs.0.868),whilecomponent-counterrordecreased from 22,504 to 3,377. Across all held-out patches, AxonSynth reduced component-count error in 10/10 patches and Euler-characteristic error in 8/10. These results show that synthetic-label domain randomization 2 E. Gaibor et al. can reduce dependence on manual axon annotation while supporting synthetic-to-real 3D segmentation.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/SASHIMI_062.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: https://openreview.net/forum?id=fraGmeHHot
BibTex
@InProceedings{GaiEdw_AxonSynth_MICCAISAT2026,
author = { Gaibor, Edward AND Bintsi, Kyriaki-Margarita AND Leiva Ureta, Carmen Luz AND Bellatif, Zayneb AND Maffei, Chiara AND Li, Wenze AND Hillman, Elizabeth AND Balbastre, Yaël AND Yendiki, Anastasia},
title = { { AxonSynth: Domain-Randomized Synthetic Data for Zero-Shot 3D Axon Segmentation in Light-Sheet Microscopy } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17258},
month = {pending},
page = {pending}
}