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

Armand Collin, Mathieu Boudreau, Julien Cohen-Adad
Proceedings of The 9th International Conference on Medical Imaging with Deep Learning, PMLR 315:3608-3627, 2026.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v315-collin26a, title = {ASTIH: A collection of axon and myelin segmentation datasets from multiple histology studies}, author = {Collin, Armand and Boudreau, Mathieu and Cohen-Adad, Julien}, booktitle = {Proceedings of The 9th International Conference on Medical Imaging with Deep Learning}, pages = {3608--3627}, year = {2026}, editor = {Huo, Yuankai and Gao, Mingchen and Kuo, Chang-Fu and Jin, Yueming and Deng, Ruining}, volume = {315}, series = {Proceedings of Machine Learning Research}, month = {08--10 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v315/main/assets/collin26a/collin26a.pdf}, url = {https://proceedings.mlr.press/v315/collin26a.html}, 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.} }
Endnote
%0 Conference Paper %T ASTIH: A collection of axon and myelin segmentation datasets from multiple histology studies %A Armand Collin %A Mathieu Boudreau %A Julien Cohen-Adad %B Proceedings of The 9th International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2026 %E Yuankai Huo %E Mingchen Gao %E Chang-Fu Kuo %E Yueming Jin %E Ruining Deng %F pmlr-v315-collin26a %I PMLR %P 3608--3627 %U https://proceedings.mlr.press/v315/collin26a.html %V 315 %X 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.
APA
Collin, A., Boudreau, M. & Cohen-Adad, J.. (2026). ASTIH: A collection of axon and myelin segmentation datasets from multiple histology studies. Proceedings of The 9th International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 315:3608-3627 Available from https://proceedings.mlr.press/v315/collin26a.html.

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