Learning to predict cutting angles from histological human brain sectionsDownload PDF

Mar 25, 2021 (edited Apr 20, 2021)MIDL 2021 Conference Short SubmissionReaders: Everyone
  • Keywords: cytoarchitecture, histology, deep learning, human brain
  • TL;DR: We present a method to predict cutting angles of histological brain sections, aiming to exclude obliquely cut tissue regions from subsequent analysis tasks.
  • Abstract: Studying brain architecture at the cellular level requires histological image analysis of sectioned postmortem samples. We trained a deep neural network to estimate relative angles between the cutting plane and the local 3D brain surface from 2D cortical image patches sampled from microscopic scans of human brain tissue sections. The model allows to automatically identify obliquely cut tissue parts, which often confuse downstream texture classification tasks and typically require specific treatment in image analysis workflows. It has immediate applications for the automated analysis of brain structures, like cytoarchitectonic mapping of the highly convoluted human brain.
  • Paper Type: validation/application paper
  • Primary Subject Area: Application: Other
  • Secondary Subject Area: Segmentation
  • Paper Status: original work, not submitted yet
  • Source Code Url: https://jugit.fz-juelich.de/experiments_cschiffer/2020_cutting_angle
  • Data Set Url: Images of histological brain sections are currently not publicly available due to infrastructure limitations resulting from the large size and number of files. 3D reconstructed data of the BigBrain used for cutting angle estimation can be found at ftp://bigbrain.loris.ca/BigBrainRelease.2015/.
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  • Authorship: I confirm that I am the author of this work and that it has not been submitted to another publication before.
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