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@ARTICLE{Schellenberg:179418,
      author       = {M. Schellenberg$^*$ and K. K. Dreher$^*$ and N.
                      Holzwarth$^*$ and F. Isensee$^*$ and A. Reinke$^*$ and N.
                      Schreck$^*$ and A. Seitel$^*$ and M. D. Tizabi$^*$ and L.
                      Maier-Hein$^*$ and J. Gröhl$^*$},
      title        = {{S}emantic segmentation of multispectral photoacoustic
                      images using deep learning.},
      journal      = {Photoacoustics},
      volume       = {26},
      issn         = {2213-5979},
      address      = {Amsterdam ˜[u.a.]œ},
      publisher    = {Elsevier},
      reportid     = {DKFZ-2022-00660},
      pages        = {100341},
      year         = {2022},
      note         = {#EA:E130#LA:E130#},
      abstract     = {Photoacoustic (PA) imaging has the potential to
                      revolutionize functional medical imaging in healthcare due
                      to the valuable information on tissue physiology contained
                      in multispectral photoacoustic measurements. Clinical
                      translation of the technology requires conversion of the
                      high-dimensional acquired data into clinically relevant and
                      interpretable information. In this work, we present a deep
                      learning-based approach to semantic segmentation of
                      multispectral photoacoustic images to facilitate image
                      interpretability. Manually annotated photoacoustic and
                      ultrasound imaging data are used as reference and enable the
                      training of a deep learning-based segmentation algorithm in
                      a supervised manner. Based on a validation study with
                      experimentally acquired data from 16 healthy human
                      volunteers, we show that automatic tissue segmentation can
                      be used to create powerful analyses and visualizations of
                      multispectral photoacoustic images. Due to the intuitive
                      representation of high-dimensional information, such a
                      preprocessing algorithm could be a valuable means to
                      facilitate the clinical translation of photoacoustic
                      imaging.},
      keywords     = {Deep learning (Other) / Medical image segmentation (Other)
                      / Multispectral imaging (Other) / Optoacoustics (Other) /
                      Photoacoustics (Other)},
      cin          = {E130 / E230 / C060},
      ddc          = {530},
      cid          = {I:(DE-He78)E130-20160331 / I:(DE-He78)E230-20160331 /
                      I:(DE-He78)C060-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:35371919},
      pmc          = {pmc:PMC8968659},
      doi          = {10.1016/j.pacs.2022.100341},
      url          = {https://inrepo02.dkfz.de/record/179418},
}