AxonCallosumEM Dataset: Axon Semantic Segmentation of Whole Corpus
  Callosum cross section from EM Images
Ao Cheng Guoqiang Zhao Lirong Wang Ruobing Zhang
The electron microscope (EM) remains the predominant technique for
elucidating intricate details of the animal nervous system at the nanometer
scale. However, accurately reconstructing the complex morphology of axons and
myelin sheaths poses a significant challenge. Furthermore, the absence of
publicly available, large-scale EM datasets encompassing complete cross
sections of the corpus callosum, with dense ground truth segmentation for axons
and myelin sheaths, hinders the advancement and evaluation of holistic corpus
callosum reconstructions. To surmount these obstacles, we introduce the
AxonCallosumEM dataset, comprising a 1.83 times 5.76mm EM image captured from
the corpus callosum of the Rett Syndrome (RTT) mouse model, which entail
extensive axon bundles. We meticulously proofread over 600,000 patches at a
resolution of 1024 times 1024, thus providing a comprehensive ground truth for
myelinated axons and myelin sheaths. Additionally, we extensively annotated
three distinct regions within the dataset for the purposes of training,
testing, and validation. Utilizing this dataset, we develop a fine-tuning
methodology that adapts Segment Anything Model (SAM) to EM images segmentation
tasks, called EM-SAM, enabling outperforms other state-of-the-art methods.
Furthermore, we present the evaluation results of EM-SAM as a baseline.