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

TL;DR

This paper introduces the AxonCallosumEM dataset, a large-scale EM image collection with detailed annotations of axons and myelin sheaths, and proposes EM-SAM, a fine-tuning approach for segmenting EM images that outperforms existing methods.

Contribution

The paper provides the first large-scale, fully annotated EM dataset of the corpus callosum and develops EM-SAM, a novel fine-tuning method for EM image segmentation based on SAM.

Findings

EM-SAM outperforms state-of-the-art segmentation methods

The dataset enables comprehensive analysis of axon and myelin morphology

Extensive annotations facilitate training and evaluation of segmentation models

Abstract

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.