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Electron microscopy (EM) enables the reconstruction of neural circuits at thelevel of individual synapses, which has been transformative for scientificdiscoveries. However, due to the complex morphology, an accurate reconstructionof cortical axons has become a major challenge. Worse still, there is nopublicly available large-scale EM dataset from the cortex that provides denseground truth segmentation for axons, making it difficult to develop andevaluate large-scale axon reconstruction methods. To address this, we introducethe AxonEM dataset, which consists of two 30x30x30 um^3 EM image volumes fromthe human and mouse cortex, respectively. We thoroughly proofread over 18,000axon instances to provide dense 3D axon instance segmentation, enablinglarge-scale evaluation of axon reconstruction methods. In addition, we denselyannotate nine ground truth subvolumes for training, per each data volume. Withthis, we reproduce two published state-of-the-art methods and provide theirevaluation results as a baseline. We publicly release our code and data athttps://connectomics-bazaar.github.io/proj/AxonEM/index.html to foster thedevelopment of advanced methods.

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