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Improving Cytoarchitectonic Segmentation of Human Brain Areas with Self-supervised Siamese Networks

  • Hannah SpitzerEmail author
  • Kai Kiwitz
  • Katrin Amunts
  • Stefan Harmeling
  • Timo Dickscheid
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11072)

Abstract

Cytoarchitectonic parcellations of the human brain serve as anatomical references in multimodal atlas frameworks. They are based on analysis of cell-body stained histological sections and the identification of borders between brain areas. The de-facto standard involves a semi-automatic, reproducible border detection, but does not scale with high-throughput imaging in large series of sections at microscopical resolution. Automatic parcellation, however, is extremely challenging due to high variation in the data, and the need for a large field of view at microscopic resolution. The performance of a recently proposed Convolutional Neural Network model that addresses this problem especially suffers from the naturally limited amount of expert annotations for training. To circumvent this limitation, we propose to pre-train neural networks on a self-supervised auxiliary task, predicting the 3D distance between two patches sampled from the same brain. Compared to a random initialization, fine-tuning from these networks results in significantly better segmentations. We show that the self-supervised model has implicitly learned to distinguish several cortical brain areas – a strong indicator that the proposed auxiliary task is appropriate for cytoarchitectonic mapping.

Keywords

Self-supervised learning Deep learning Brain parcellation Human brain Histology 

Notes

Acknowledgements

This work was partially supported by the Helmholtz Association through the Helmholtz Portfolio Theme “Supercomputing and Modeling for the Human Brain”, and by the European Union’s Horizon 2020 Framework Research and Innovation under Grant Agreement No. 7202070 (Human Brain Project SGA1) and 785907 (Human Brain Project SGA2). Computing time was granted by the John von Neumann Institute for Computing (NIC) and provided on the supercomputer JURECA at Jülich Supercomputing Centre (JSC).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Hannah Spitzer
    • 1
    Email author
  • Kai Kiwitz
    • 2
  • Katrin Amunts
    • 1
    • 2
  • Stefan Harmeling
    • 3
  • Timo Dickscheid
    • 1
  1. 1.Institute of Neuroscience and Medicine INM-1Forschungszentrum JülichJülichGermany
  2. 2.C. and O. Vogt Institute of Brain ResearchHeinrich-Heine University DüsseldorfDüsseldorfGermany
  3. 3.Institute of Computer ScienceHeinrich-Heine University DüsseldorfDüsseldorfGermany

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