ASTIH: A collection of axon and myelin segmentation datasets from multiple histology studies

Armand Collin, Mathieu Boudreau, Julien Cohen-Adad

Keywords: Histology, Axon, Myelin, Open Dataset, Segmentation, Microscopy.

TL;DR: ASTIH provides a standardized benchmark of over 69,000 manually segmented fibers across diverse modalities (TEM, SEM, Bright-field) and species to facilitate high-throughput neurohistological analysis.

Abstract: Large-scale analysis of axon and myelin morphometry in nervous tissues is fundamental to neuroscience research, yet manual quantification remains a profound bottleneck, limiting the scale and efficiency of studies. To address this, we introduce the Axon Segmentation Training Initiative for Histology (ASTIH), a publicly accessible resource designed to propel the development and validation of automated histomorphometry tools. ASTIH comprises five meticulously curated datasets, standardized for machine learning applications, featuring over 69,000 manually segmented axon fibers. These datasets exhibit significant diversity, spanning three microscopy modalities (TEM, SEM, bright-field), three species (mouse, rat, rabbit), and three distinct anatomical regions (brain, spinal cord, peripheral nerves) with varying pixel resolutions (from ~0.2 to 0.002 $\mu m/px$). All datasets contain detailed annotations with standardized boundary delineation between adjacent fibers, enabling effective use for both semantic and instance segmentation tasks. We also provide thoroughly evaluated baseline segmentation models for every dataset in the collection to facilitate future benchmarking.

Primary Subject Area: Segmentation

Secondary Subject Area: Application: Neuroimaging

Registration Requirement: Yes

Reproducibility: https://axondeepseg.github.io/ASTIH/

Visa & Travel: No

Read CFP & Author Instructions: Yes

Originality Policy: Yes

Single-blind & Not Under Review Elsewhere: Yes

LLM Policy: Yes

Submission Number: 336