% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@INPROCEEDINGS{Huysegoms:1033599,
      author       = {Huysegoms, Marcel and Bludau, Sebastian and Upschulte, Eric
                      and Dickscheid, Timo and Amunts, Katrin},
      title        = {{C}ellular level 3{D} reconstructed volumes at 1µm
                      resolution within the {B}ig{B}rain},
      reportid     = {FZJ-2024-06479},
      year         = {2024},
      abstract     = {<b>Background $\&$ Summary</b><br>Analyzing cells and their
                      distributions in histological sections is the basis for
                      computing cytoarchitectonic maps of the human brain (Amunts
                      and Zilles, 2015). While cell distributions are inherently
                      3-dimensional, microscopic analysis in cell-body stained
                      tissue sections is usually performed in individual 2D
                      sections. The first 3D reconstruction of an entire human
                      brain from histology was generated at 20µm isotropic
                      resolution and shared as ‘BigBrain’ (Amunts et al.,
                      2013). However, investigating the distribution of individual
                      cells in 3D requires even higher resolution and
                      precision.<br><br>While previous work has exploited
                      trajectories of vessels for cross-section alignment
                      (Dickscheid et al., 2019), bisected cells provide
                      significantly more alignment constraints and overall better
                      coverage of tissue, thus allowing improved precision of
                      image registration and correction of 3D cell distributions
                      with respect to redundant detections (Huysegoms et al.,
                      2019). Based on this strategy, we processed 300 histological
                      sections from the occipital cortex of the BigBrain dataset,
                      which were scanned at 1µm isotropic resolution. We
                      reconstructed two 6x6x6 mm3 volumes of interest in 3D; one
                      in V1 (h0c1) and the other in V2 (h0c2). Both volumes were
                      subsequently anchored into the 20µm 3D BigBrain space using
                      an affine transformation based on manually selected
                      landmarks defined with VoluBA
                      (https://ebrains.eu/service/voluba). The provided resources
                      benefit evaluation of workflows that analyze 3D cell
                      distributions.<br><br><b>Data acquisition</b><br>300
                      coronal, cell-body stained histological sections (20µm
                      thickness) of the BigBrain were imaged with a
                      high-throughput scanning system (Tissue Scope, Huron
                      Technologies, Inc.) at 20 different focal planes, each with
                      an inplane resolution of 1µm. A nearly isotropic image
                      stack was thus obtained within the occipital cortex, ranging
                      from section number 429 to 728 of the BigBrain dataset. As
                      described in detail in (Amunts et al., 2013), sections were
                      embedded in paraffin and stained using a modified silver
                      staining. Consequently, the images show cell bodies with a
                      strong dark contrast.Each section was visually inspected for
                      histological artifacts. Eight sections were excluded from
                      volume 1 and nine sections were excluded from volume
                      2.<br><br><b>Detection and matching of
                      microstructures</b><br>The workflow used for reconstructing
                      the stack of histological images is based on the detection
                      and matching of corresponding microstructures between
                      consecutive sections. Specifically, we trained a Support
                      Vector Machine to detect blood vessels and developed a cell
                      segmentation algorithm based on Deep Learning that is robust
                      to staining inhomogeneities and overlapping cells (Upschulte
                      et al., 2022). Separating touching and overlapping cells
                      poses an especially challenging problem in segmentation
                      tasks but is resolved in the present work by incorporating
                      a-priori knowledge of cellular contours.We were subsequently
                      able to identify corresponding pairs of bisected cells
                      between adjacent sections using their centroid positions.
                      The matching task is challenged by the limited
                      distinctiveness of cell shapes, as well as the relatively
                      low prevalence of bisected cells $(20-40\%$ of cells at
                      20µm tissue thickness). To overcome these issues, our
                      workflow computes the Largest Common Pointset (LCP) between
                      cell centroids of adjacent sections, using purely geometric
                      constraints under locally affine transforms. The approach is
                      based on the 4-Points Congruent Sets strategy (Aiger, Mitra
                      and Cohen-Or, 2008), which repeatedly selects 4 random
                      points in one pointset and finds approximately congruent
                      point constellations in the other, effectively identifying
                      valid pairs of bisected cells. To deal with large human
                      brain sections, we extended the algorithm to operate
                      hierarchically across multiple scales and incorporated a
                      sliding window approach to handle non-linear tissue
                      deformations (Huysegoms et al., 2019).<br><br><b>Linear 3D
                      reconstruction</b><br>Based on the extracted cell matches,
                      an optimal affine 2D transformation was computed for each
                      pair of consecutive images using a Least Squares approach.
                      The resulting transformations were concatenated in an
                      iterative manner in order to yield absolute positions.
                      During this process, we applied a user-defined ROI of size
                      6x6mm2 to filter the number of matches and to obtain affine
                      parameters that are tailored to the local tissue
                      deformations.<br><br><b>Acknowledgements</b><br>Pavel
                      Chervakov and Xiao Gui (both INM-1) built and generally
                      maintain the infrastructure to provide interactive online
                      visualization of the reconstructed datasets.<br>This project
                      received funding from the European Union’s Horizon 2020
                      Research and Innovation Programme [grant agreement 785907
                      (HBP SGA2), 945539 (HBP SGA3)] and the Helmholtz
                      Association’s Initiative and Networking Fund through the
                      Helmholtz International BigBrain Analytics and Learning
                      Laboratory (HIBALL) [grant agreement
                      InterLabs-0015].<br>Computing time was granted through JARA
                      on the supercomputer JURECA at Jülich Supercomputing Centre
                      (JSC) as part of the project CJINM14.},
      month         = {Nov},
      date          = {2024-11-19},
      organization  = {INM Retreat 2024, Jülich (Germany),
                       19 Nov 2024 - 20 Nov 2024},
      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)
                      / EBRAINS 2.0 - EBRAINS 2.0: A Research Infrastructure to
                      Advance Neuroscience and Brain Health (101147319) / HBP SGA3
                      - Human Brain Project Specific Grant Agreement 3 (945539)},
      pid          = {G:(DE-HGF)POF4-5251 / G:(DE-HGF)POF4-5254 /
                      G:(DE-HGF)InterLabs-0015 / G:(EU-Grant)101147319 /
                      G:(EU-Grant)945539},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/1033599},
}