Kai Kiwitz

Kai Kiwitz
Heinrich-Heine-Universität Düsseldorf | HHU · Cécile and Oskar Vogt Institute of Brain Research

M. Sc.
High-Resolution Brain Mapping

About

6
Publications
768
Reads
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40
Citations
Introduction
Kai evalutes the reliability and validity of automatic brain mapping approaches. Currently he investigates how artificial neural networks operate on tasks used to segment the brain based on its cytoarchitecture. Coming from a background of cognitive neuroscience he participates in developing high-resolution reference atlases of the human brain.
Additional affiliations
June 2019 - December 2021
Max Planck School of Cognition
Position
  • PhD Student
Description
  • 0-year PhD student at the Max Planck School of Cognition
October 2018 - present
Heinrich-Heine-Universität Düsseldorf
Position
  • Lecturer
Description
  • Lecturer on functional systems, Translational Neuroscience Master program
April 2017 - present
Heinrich-Heine-Universität Düsseldorf
Position
  • Neuroanatomy Tutor
Description
  • Tutor in functional and structural properties of macro-anatomical structures of the brain, Psychology Master program
Education
December 2016 - November 2019
Heinrich-Heine-Universität Düsseldorf
Field of study
  • Neuroscience
October 2014 - November 2016
Ruhr-Universität Bochum
Field of study
  • Psychology and Cognitive Neuroscience
October 2011 - September 2014

Publications

Publications (6)
Article
Full-text available
The human metathalamus plays an important role in processing visual and auditory information. Understanding its layers and subdivisions is important to gain insights in its function as a subcortical relay station and involvement in various pathologies. Yet, detailed histological references of the microanatomy in 3D space are still missing. We there...
Article
Full-text available
Human brain atlases provide spatial reference systems for data characterizing brain organization at different levels, coming from different brains. Cytoarchitecture is a basic principle of the microstructural organization of the brain, as regional differences in the arrangement and composition of neuronal cells are indicators of changes in connecti...
Article
Full-text available
The distribution of neurons in the cortex (cytoarchitecture) differs between cortical areas and constitutes the basis for structural maps of the human brain. Deep learning approaches provide a promising alternative to overcome throughput limitations of currently used cytoarchitectonic mapping methods, but typically lack insight as to what extent th...
Preprint
Human brain atlases provide spatial reference systems for data characterizing brain organization at different levels, coming from different brains. Cytoarchitecture is a basic principle of the microstructural organization of the brain, as regional differences in the arrangement and composition of neuronal cells are indicators of changes in connecti...
Chapter
Full-text available
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-th...
Preprint
Full-text available
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-th...

Network

Cited By

Projects

Project (1)
Project
We are working on reliably automating cytoarchitectonic brain mapping approaches using deep learning techniques. Our main goal is to understand how deep learning solves complex segmentation problems on high-resolution histoloigcal data. Using this knowledge we want to create high resolution brain maps of the visual cortex.