  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.
