Paper Info
Title
AxonEM Dataset: 3D Axon Instance Segmentation of Brain Cortical Regions
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 30 x 30 x 30 mu m(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.
Year
DOI
Venue
2021
10.1007/978-3-030-87193-2_17
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2021, PT I
Keywords
DocType
Volume
Axon, Electron microscopy, 3D instance segmentation
Conference
12901
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
0
16
Name
Order
Citations
PageRank
Donglai Wei120011.80
k lee2224.61
Hanyu Li365.58
Ran Lu410.71
J. Alexander Bae500.34
Zequan Liu600.34
Lifu Zhang700.34
Márcia dos Santos800.34
Lin Zudi902.37
Thomas Uram1000.34
Xueying Wang1112.39
Ignacio Arganda-Carreras1200.34
Brian Matejek13123.54
Narayanan Kasthuri14627.11
Jeff W. Lichtman1513412.41
Hanspeter Pfister165933340.59