• English
  • Deutsch
  • Log In
    Password Login
    or
  • Research Outputs
  • Projects
  • Researchers
  • Institutes
  • Statistics
Repository logo
Fraunhofer-Gesellschaft
  1. Home
  2. Fraunhofer-Gesellschaft
  3. Artikel
  4. Deep Learning to Decipher the Progression and Morphology of Axonal Degeneration
 
  • Details
  • Full
Options
2021
Journal Article
Titel

Deep Learning to Decipher the Progression and Morphology of Axonal Degeneration

Abstract
Axonal degeneration (AxD) is a pathological hallmark of many neurodegenerative diseases. Deciphering the morphological patterns of AxD will help to understand the underlying mechanisms and develop effective therapies. Here, we evaluated the progression of AxD in cortical neurons using a novel microfluidic device together with a deep learning tool that we developed for the enhanced-throughput analysis of AxD on microscopic images. The trained convolutional neural network (CNN) sensitively and specifically segmented the features of AxD including axons, axonal swellings, and axonal fragments. Its performance exceeded that of the human evaluators. In an in vitro model of AxD in hemorrhagic stroke induced by the hemolysis product hemin, we detected a time-dependent degeneration of axons leading to a decrease in axon area, while axonal swelling and fragment areas increased. Axonal swellings preceded axon fragmentation, suggesting that swellings may be reliable predictors of AxD. Using a recurrent neural network (RNN), we identified four morphological patterns of AxD (granular, retraction, swelling, and transport degeneration). These findings indicate a morphological heterogeneity of AxD in hemorrhagic stroke. Our EntireAxon platform enables the systematic analysis of axons and AxD in time-lapse microscopy and unravels a so-far unknown intricacy in which AxD can occur in a disease context.
Author(s)
Palumbo, A.
GrĂ¼ning, P.
Landt, S.K.
Heckmann, L.E.
Bartram, L.
Pabst, A.
Flory, C.
Ikhsan, M.
Pietsch, S.
Schulz, R.
Kren, C.
Koop, N.
Boltze, J.
Mamlouk, A.M.
Zille, M.
Zeitschrift
Cells
Thumbnail Image
DOI
10.3390/cells10102539
Externer Link
Externer Link
Language
English
google-scholar
EMB
  • Cookie settings
  • Imprint
  • Privacy policy
  • Api
  • Send Feedback
© 2022