Axon and Myelin Sheath Segmentation in Electron Microscopy Images using Meta Learning

Various neurological diseases affect the morphology of myelinated axons. Quantitative analysis of these structures and changes occurring due to neurodegeneration or neuroregeneration is of great importance for characterization of disease state and treatment response. This paper proposes a robust, meta-learning based pipeline for segmentation of axons and surrounding myelin sheaths in electron microscopy images. This is the first step towards computation of electron microscopy related bio-markers of hypoglossal nerve degeneration/regeneration. This segmentation task is challenging due to large variations in morphology and texture of myelinated axons at different levels of degeneration and very limited availability of annotated data. To overcome these difficulties, the proposed pipeline uses a meta learning-based training strategy and a U-net like encoder decoder deep neural network. Experiments on unseen test data collected at different magnification levels (i.e, trained on 500X and 1200X images, and tested on 250X and 2500X images) showed improved segmentation performance by 5% to 7% compared to a regularly trained, comparable deep learning network.

I. Introduction

An axon is a cable-like structure that extends from a neuron. Axons are responsible for the transmission of electrical signals between neurons and peripheral tissues. In the central and peripheral nervous systems, most axons are wrapped in a spiral fashion with a myelin sheath [1], as illustrated in Figure 1. A myelin sheath is an extended and modified plasma membrane that functions as an insulator [2]. Various neurological diseases affect the morphology of axons and the myelin sheaths surrounding them. High-resolution electron microscopy is used to capture the subtle morphological changes in the nervous system in general and in myelinated axons in particular (Figure 2). Quantitative analysis of these structures is of great importance for characterization of disease state and treatment response [3]–[6]. Image segmentation is the first step towards this goal. Various segmentation methods using deep learning approaches have been proposed for segmentation of axons and myelin sheaths around them. For example, [7] created a framework based on U-net for segmentation of axons and myelin sheaths in 2D images of scanning electron microscopy (SEM) and transmission electron microscopy (TEM); [8]–[10] proposed a software package using a deep neural network for 3D instance segmentation of axons and myelin; [11] introduced a 3D image dataset, AxonEM, for instance segmentation of axons in brain cortical regions. These models rely on the availability of large amounts of annotated data for training. This is a problem due to: (1) labor-intensive and expertise-required nature of the image segmentation process; (2) limited availability of diseased samples; (3) different imaging parameters for the existing data; and (4) large appearance variations in the data caused by neurodegeneration/regeneration.