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The electron microscope (EM) remains the predominant technique forelucidating intricate details of the animal nervous system at the nanometerscale. However, accurately reconstructing the complex morphology of axons andmyelin sheaths poses a significant challenge. Furthermore, the absence ofpublicly available, large-scale EM datasets encompassing complete crosssections of the corpus callosum, with dense ground truth segmentation for axonsand myelin sheaths, hinders the advancement and evaluation of holistic corpuscallosum reconstructions. To surmount these obstacles, we introduce theAxonCallosumEM dataset, comprising a 1.83 times 5.76mm EM image captured fromthe corpus callosum of the Rett Syndrome (RTT) mouse model, which entailextensive axon bundles. We meticulously proofread over 600,000 patches at aresolution of 1024 times 1024, thus providing a comprehensive ground truth formyelinated axons and myelin sheaths. Additionally, we extensively annotatedthree distinct regions within the dataset for the purposes of training,testing, and validation. Utilizing this dataset, we develop a fine-tuningmethodology that adapts Segment Anything Model (SAM) to EM images segmentationtasks, called EM-SAM, enabling outperforms other state-of-the-art methods.Furthermore, we present the evaluation results of EM-SAM as a baseline.

AxonCallosumEM Dataset: Axon Semantic Segmentation of Whole Corpus Callosum cross section from EM Images