% 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”.

@ARTICLE{Xu:276118,
      author       = {Xu, Marshall and Ribeiro, Fernanda L. and Barth, Markus and
                      Bernier, Michaël and Bollmann, Steffen and Chatterjee,
                      Soumick and Cognolato, Francesco and Gulban, Omer F. and
                      Itkyal, Vaibhavi and Liu, Siyu and Mattern, Hendrik and
                      Polimeni, Jonathan R. and Shaw, Thomas B. and Speck, Oliver
                      and Bollmann, Saskia},
      title        = {{V}essel{B}oost: {A} {P}ython {T}oolbox for {S}mall {B}lood
                      {V}essel {S}egmentation in {H}uman {M}agnetic {R}esonance
                      {A}ngiography {D}ata},
      journal      = {Aperture neuro},
      volume       = {4},
      issn         = {2957-3963},
      address      = {Roseville},
      publisher    = {Organization for Human Brain Mapping},
      reportid     = {DZNE-2025-00199},
      pages        = {10.52294/001c.123217},
      year         = {2024},
      abstract     = {Magnetic resonance angiography (MRA) performed at
                      ultra-high magnetic field provides a unique opportunity to
                      study the arteries of the living human brain at the
                      mesoscopic level. From this, we can gain new insights into
                      the brain’s blood supply and vascular disease affecting
                      small vessels. However, for quantitative characterization
                      and precise representation of human angioarchitecture to,
                      for example, inform blood-flow simulations, detailed
                      segmentations of the smallest vessels are required. Given
                      the success of deep learning-based methods in many
                      segmentation tasks, we explore their application to
                      high-resolution MRA data and address the difficulty of
                      obtaining large data sets of correctly and comprehensively
                      labelled data. We introduce VesselBoost, a vessel
                      segmentation toolbox, which utilizes deep learning and
                      imperfect training labels for accurate vasculature
                      segmentation. To enhance the segmentation models’
                      robustness and accuracy, VesselBoost employs an innovative
                      data augmentation technique, which captures the resemblance
                      of vascular structures across scales by zooming in or out on
                      input image patches—virtually creating diverse scale
                      vascular data. This approach enables detailed vascular
                      segmentation and ensures the model’s ability to generalize
                      across various scales of vascular structures.},
      cin          = {AG Speck / AG Schreiber},
      cid          = {I:(DE-2719)1340009 / I:(DE-2719)1310010},
      pnm          = {353 - Clinical and Health Care Research (POF4-353)},
      pid          = {G:(DE-HGF)POF4-353},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.52294/001c.123217},
      url          = {https://pub.dzne.de/record/276118},
}