AxonEM Dataset: 3D Axon Instance Segmentation of Brain Cortical Regions

TL;DR

The AxonEM dataset provides the first large-scale, densely annotated 3D EM images of cortical axons from human and mouse brains, enabling improved development and evaluation of axon reconstruction methods.

Contribution

We introduce the AxonEM dataset with dense 3D axon segmentation, including annotations for over 18,000 axons, and evaluate existing methods to establish baselines.

Findings

Reproduced two state-of-the-art axon reconstruction methods.

Provided dense ground truth annotations for large-scale evaluation.

Publicly released dataset and code to support future research.

Abstract

Electron microscopy (EM) enables the reconstruction of neural circuits at the level of individual synapses, which has been transformative for scientific discoveries. However, due to the complex morphology, an accurate reconstruction of cortical axons has become a major challenge. Worse still, there is no publicly available large-scale EM dataset from the cortex that provides dense ground truth segmentation for axons, making it difficult to develop and evaluate large-scale axon reconstruction methods. To address this, we introduce the AxonEM dataset, which consists of two 30x30x30 um^3 EM image volumes from the human and mouse cortex, respectively. We thoroughly proofread over 18,000 axon instances to provide dense 3D axon instance segmentation, enabling large-scale evaluation of axon reconstruction methods. In addition, we densely annotate nine ground truth subvolumes for training, per each data volume. With this, we reproduce two published state-of-the-art methods and provide their evaluation results as a baseline. We publicly release our code and data at https://connectomics-bazaar.github.io/proj/AxonEM/index.html to foster the development of advanced methods.