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@ARTICLE{Adler:143340,
      author       = {T. J. Adler$^*$ and L. Ardizzone and A. Vemuri$^*$ and L.
                      Ayala$^*$ and J. Gröhl$^*$ and T. Kirchner$^*$ and S.
                      Wirkert$^*$ and J. Kruse and C. Rother and U. Köthe and L.
                      Maier-Hein$^*$},
      title        = {{U}ncertainty-aware performance assessment of optical
                      imaging modalities with invertible neural networks.},
      journal      = {International journal of computer assisted radiology and
                      surgery},
      volume       = {14},
      number       = {6},
      issn         = {1861-6429},
      address      = {Heidelberg [u.a.]},
      publisher    = {Springer},
      reportid     = {DKFZ-2019-00930},
      pages        = {997-1007},
      year         = {2019},
      abstract     = {Optical imaging is evolving as a key technique for advanced
                      sensing in the operating room. Recent research has shown
                      that machine learning algorithms can be used to address the
                      inverse problem of converting pixel-wise multispectral
                      reflectance measurements to underlying tissue parameters,
                      such as oxygenation. Assessment of the specific hardware
                      used in conjunction with such algorithms, however, has not
                      properly addressed the possibility that the problem may be
                      ill-posed.We present a novel approach to the assessment of
                      optical imaging modalities, which is sensitive to the
                      different types of uncertainties that may occur when
                      inferring tissue parameters. Based on the concept of
                      invertible neural networks, our framework goes beyond point
                      estimates and maps each multispectral measurement to a full
                      posterior probability distribution which is capable of
                      representing ambiguity in the solution via multiple modes.
                      Performance metrics for a hardware setup can then be
                      computed from the characteristics of the
                      posteriors.Application of the assessment framework to the
                      specific use case of camera selection for physiological
                      parameter estimation yields the following insights: (1)
                      estimation of tissue oxygenation from multispectral images
                      is a well-posed problem, while (2) blood volume fraction may
                      not be recovered without ambiguity. (3) In general,
                      ambiguity may be reduced by increasing the number of
                      spectral bands in the camera.Our method could help to
                      optimize optical camera design in an application-specific
                      manner.},
      cin          = {E130 / E131},
      ddc          = {610},
      cid          = {I:(DE-He78)E130-20160331 / I:(DE-He78)E131-20160331},
      pnm          = {315 - Imaging and radiooncology (POF3-315)},
      pid          = {G:(DE-HGF)POF3-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:30903566},
      doi          = {10.1007/s11548-019-01939-9},
      url          = {https://inrepo02.dkfz.de/record/143340},
}