AxonSynth: Domain-Randomized Synthetic Data for Zero-Shot 3D Axon Segmentation in Light-Sheet Microscopy
Paper recorded by Signals 4 on 2026-09-25 in cs.CV. Abstract reproduced from arXiv; link to the original below.
Published 2026-09-25 on arXiv · recorded by Signals 4 on 2026-09-28
Category: cs.CV · 计算机视觉 · first seen 2026-09-28
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. We present AxonSynth, a domain-randomized synthetic-data framework f
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Cite this page: AxonSynth: Domain-Randomized Synthetic Data for Zero-Shot 3D Axon Segmentation in Light-Sheet Microscopy: the #29 most recent of 342 cs.CV papers we have recorded (as of 2026-09-25). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/axonsynth-domain-randomized-synthetic-data-for-zero-shot-3d-axon-segmentation-in.html
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