4.5 Article

AxonSeg: Open Source Software for Axon and Myelin Segmentation and Morphometric Analysis

期刊

FRONTIERS IN NEUROINFORMATICS
卷 10, 期 -, 页码 -

出版社

FRONTIERS MEDIA SA
DOI: 10.3389/fninf.2016.00037

关键词

axon; myelin; segmentation; discriminant analysis; histology; microscopy; graphical user interface; g-ratio

资金

  1. Canada Research Chair in Quantitative Magnetic Resonance Imaging [230815]
  2. Canadian Institute of Health Research [CIHR FDN-143263]
  3. Fonds de Recherche du Quebec-Sante [28826]
  4. Fonds de Recherche du Quebec Nature et Technologies [2015-PR-182754]
  5. Natural Sciences and Engineering Research Council of Canada [435897-2013]

向作者/读者索取更多资源

Segmenting axon and myelin from microscopic images is relevant for studying the peripheral and central nervous system and for validating new MRI techniques that aim at quantifying tissue microstructure. While several software packages have been proposed, their interface is sometimes limited and/or they are designed to work with a specific modality (e.g., scanning electron microscopy (SEM) only). Here we introduce AxonSeg, which allows to perform automatic axon and myelin segmentation on histology images, and to extract relevant morphometric information, such as axon diameter distribution, axon density and the myelin g-ratio. AxonSeg includes a simple and intuitive MATLABbased graphical user interface (GUI) and can easily be adapted to a variety of imaging modalities. The main steps of AxonSeg consist of: (i) image pre-processing; (ii) pre-segmentation of axons over a cropped image and discriminant analysis (DA) to select the best parameters based on axon shape and intensity information; (iii) automatic axon and myelin segmentation over the full image; and (iv) atlas-based statistics to extract morphometric information. Segmentation results from standard optical microscopy (OM), SEM and coherent anti-Stokes Raman scattering (CARS) microscopy are presented, along with validation against manual segmentations. Being fully-automatic after a quick manual intervention on a cropped image, we believe AxonSeg will be useful to researchers interested in large throughput histology.

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