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@ARTICLE{Baumann:305208,
      author       = {A. Baumann$^*$ and L. Ayala$^*$ and A. Studier-Fischer$^*$
                      and J. Sellner$^*$ and B. Özdemir$^*$ and K.-F.
                      Kowalewski$^*$ and S. Ilic and S. Seidlitz$^*$ and L.
                      Maier-Hein$^*$},
      title        = {{N}eural illumination calibration for surgical
                      workflow-optimized spectral imaging.},
      journal      = {International journal of computer assisted radiology and
                      surgery},
      volume       = {nn},
      issn         = {1861-6410},
      address      = {Heidelberg [u.a.]},
      publisher    = {Springer},
      reportid     = {DKFZ-2025-02056},
      pages        = {nn},
      year         = {2025},
      note         = {#EA:E130#LA:E130# / epub},
      abstract     = {Hyperspectral imaging (HSI) is emerging as a promising
                      novel imaging modality with various potential surgical
                      applications. Currently available cameras, however, suffer
                      from poor integration into the clinical workflow because
                      they require the lights to be switched off or the camera to
                      be manually recalibrated as soon as lighting conditions
                      change.We propose a novel learning-based approach to
                      recalibration of hyperspectral cameras during surgery that
                      predicts the corresponding white reference image from an
                      uncalibrated hyperspectral input, enabling spatially
                      resolved, automatic, and sterile calibration under varying
                      illumination conditions. Our key novelty lies in (i) the
                      disentanglement of the space of possible illuminations from
                      the space of possible tissue configurations and (ii)
                      combining real-world white reference measurements with
                      physics-inspired simulated illuminations to create a diverse
                      and representative training set.Based on a total of 1,890
                      HSI cubes from a phantom, porcine subjects, rats, and
                      humans, we derive the following key insights: Firstly,
                      dynamically changing lighting conditions in the operating
                      room dramatically reduce the performance of methods for
                      physiological parameter estimation and surgical scene
                      segmentation. Secondly, our method is not only sufficiently
                      accurate to replace the tedious process of white
                      reference-based recalibration, but also outperforms
                      previously proposed methods by a large margin. Finally, our
                      approach generalizes across species, lighting conditions,
                      and image processing tasks.Our method enables seamless
                      integration of hyperspectral imaging into surgical workflows
                      by providing rapid and automated illumination calibration.
                      Its robust generalization across diverse conditions
                      significantly enhances the reliability and practicality of
                      spectral imaging in clinical settings, paving the way for
                      broader adoption of HSI in surgery.},
      keywords     = {Deep Learning (Other) / Hyperspectral Imaging (Other) /
                      Illumination Calibration (Other) / Intra-Operative Imaging
                      (Other)},
      cin          = {E130 / E140 ; E140},
      ddc          = {610},
      cid          = {I:(DE-He78)E130-20160331 / I:(DE-He78)E140-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:41055831},
      doi          = {10.1007/s11548-025-03525-8},
      url          = {https://inrepo02.dkfz.de/record/305208},
}