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. 2022 Oct:2022:10.1109/aipr57179.2022.10092238.
doi: 10.1109/aipr57179.2022.10092238. Epub 2023 Apr 10.

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

Nguyen P Nguyen  1 , Stephanie Lopez  2 , Catherine L Smith  2 , Teresa E Lever  2 , Nicole L Nichols  2 , Filiz Bunyak  1
Affiliations

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

Nguyen P Nguyen et al. IEEE Appl Imag Pattern Recognit Workshop. 2022 Oct.

Abstract

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.

Keywords: axon; electron microscopy; meta learning; myelin.

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Figures

Fig. 1:
Fig. 1:
Neuron anatomy illustrated by using BioRender app [19]
Fig. 2:
Fig. 2:
Electron microscopy images of axons and myelin sheaths surrounding them at different magnification levels: (A) 500X, (B) 1200X, and (C)2500X.
Fig. 3:
Fig. 3:
General training pipeline of meta learning for deep neural network.
Fig. 4:
Fig. 4:
Unrolling gradient updates between inner loop and outer loop in MAML [23], [29].
Fig. 5:
Fig. 5:
Segmentation outputs for training and test stages: axons (yellow areas) and myelin (purple areas). Blue arrow: false negative, red arrow: false positive.
See this image and copyright information in PMC

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