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@INPROCEEDINGS{Mohlberg:1018405,
      author       = {Mohlberg, Hartmut and Lepage, Y. Claude and Toussaint,
                      Paule-J. and Wenzel, Susanne and Evans, Alan C. and Amunts,
                      Katrin},
      title        = {3{D} reconstruction of {B}ig{B}rain2: {P}rogress report on
                      updated processing pipeline and application to existing
                      annotations and cortical surfaces},
      reportid     = {FZJ-2023-04786},
      year         = {2023},
      abstract     = {The development of BigBrain2 is a continuation of the first
                      BigBrain [1] that will contribute new insight on
                      inter-subject cytoarchitectonic variability. Overall,
                      BigBrain2 offers better quality staining, favorable to
                      regional segmentation and registration, and contains fewer
                      artefacts through sectioning and staining. In this
                      presentation, we will report about the initial 3D
                      reconstruction of BigBrain2 at 100µm, which is suitable
                      already for the extraction of cortical surfaces and the
                      representation of annotations of some cortical and
                      subcortical regions.The paraffin embedded fixed brain of a
                      30-year-old male donor was sectioned coronally at 20µm
                      thickness using a large-scale microtome. All 7676 sections
                      were stained for cell bodies (Merker stain), then scanned at
                      10µm in-plane (flatbed scanner, 8bit grey level encoding)
                      and subsequently at 1µm in-plane (Huron TissueScope
                      scanner). The histological flatbed scanner sections were
                      resampled at 20µm in-plane, to match the section thickness,
                      and manual and semi-automatic corrections were performed to
                      repair acquisition artifacts due to sectioning and
                      histological preparation (tears, folds, missing tissue,
                      excessive distortion etc.) [2]. Every fifth section was
                      initially repaired, with comprehensive quality control (QC),
                      from which a first 3D reconstruction was obtained at an
                      effective section spacing of 100µm. Data provenance
                      tracking of all repair operations provides a means for
                      assessing the extents of the repaired artifacts and for
                      eventual reproducibility at the 1µm in-plane resolution.
                      The repaired sections were aligned to the post-mortem MRI of
                      the fixed brain (Siemens Sonata, 1.5T, MPRAGE, 0.5mm) in an
                      iterative process by 3D registration of the stacked images
                      to the MRI, followed by 2D registration of the individual
                      images to the sliced MRI, while gradually increasing the
                      degree of 2D and 3D registration from rigid-body to affine
                      to non-linear across 10 global iterations. These extra
                      global iterations helped resolve the lower-frequency
                      alignment errors causing jaggies. Alignment to the MRI
                      enables to correct for tissue compression caused by cutting
                      and mounting of sections, and tissue shrinkage. Ultimately,
                      section-to-section non-linear 2D alignment (without MRI) was
                      performed to resolve high-frequency alignment errors.
                      Optical-balancing was applied by normalizing image
                      intensities to the MRI data to correct for staining
                      imbalances across the brain. The reconstructed 3D volume is
                      obtained at 100µm in the MRI ex-vivo space, which is
                      suitable for the extraction of cortical surfaces. Finally,
                      computed transformations are saved and can be applied to
                      regions annotated on the original sections.Ongoing work
                      includes the semi-automatic repairs of the remaining
                      sections $(80\%)$ to obtain a complete volume at 20µm
                      isotropic resolution onto which sections at the cellular
                      resolution of 1µm can be progressively
                      overlaid.References:[1] Amunts K. et al., BigBrain: An
                      Ultrahigh-Resolution 3D Human Brain Model. Science, 2013.[2]
                      Mohlberg H. et al., 3D reconstruction of BigBrain2:
                      Challenges, methods, and status of histological section
                      repair – A progress report. BigBrain Workshop 2022},
      month         = {Oct},
      date          = {2023-10-04},
      organization  = {7th BigBrain Workshop, Reykjavík
                       (Iceland), 4 Oct 2023 - 6 Oct 2023},
      subtyp        = {After Call},
      cin          = {INM-1},
      cid          = {I:(DE-Juel1)INM-1-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525) / 5254 - Neuroscientific Data Analytics and AI
                      (POF4-525) / HIBALL - Helmholtz International BigBrain
                      Analytics and Learning Laboratory (HIBALL) (InterLabs-0015)},
      pid          = {G:(DE-HGF)POF4-5251 / G:(DE-HGF)POF4-5254 /
                      G:(DE-HGF)InterLabs-0015},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/1018405},
}