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The Active Atlas: Combining 3D Anatomical Models with Texture Detectors

  • Yuncong ChenEmail author
  • Lauren McElvain
  • Alex Tolpygo
  • Daniel Ferrante
  • Harvey Karten
  • Partha Mitra
  • David Kleinfeld
  • Yoav Freund
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10433)

Abstract

While modern imaging technologies such as fMRI have opened exciting possibilities for studying the brain in vivo, histological sections remain the best way to study brain anatomy at the level of neurons. The procedure for building histological atlas changed little since 1909 and identifying brain regions is a still a labor intensive process performed only by experienced neuroanatomists. Existing digital atlases such as the Allen Reference Atlas are constructed using downsampled images and can not reliably map low-contrast parts such as brainstem, which is usually annotated based on high-resolution cellular texture.

We have developed a digital atlas methodology that combines information about the 3D organization and the detailed texture of different structures. Using the methodology we developed an atlas for the mouse brainstem, a region for which there are currently no good atlases. Our atlas is “active” in that it can be used to automatically align a histological stack to the atlas, thus reducing the work of the neuroanatomist.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yuncong Chen
    • 1
    Email author
  • Lauren McElvain
    • 2
  • Alex Tolpygo
    • 3
  • Daniel Ferrante
    • 3
  • Harvey Karten
    • 2
  • Partha Mitra
    • 3
  • David Kleinfeld
    • 2
  • Yoav Freund
    • 1
  1. 1.Department of Computer Science and EngineeringUniversity of CaliforniaSan Diego, La JollaUSA
  2. 2.Department of PhysicsUniverity of CaliforniaSan Diego, La JollaUSA
  3. 3.Cold Spring Harbor LaboratoryCold Spring HarborUSA

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