TY  - JOUR
AU  - Adler, Tim J
AU  - Ardizzone, Lynton
AU  - Vemuri, Anant
AU  - Ayala, Leonardo
AU  - Gröhl, Janek
AU  - Kirchner, Thomas
AU  - Wirkert, Sebastian
AU  - Kruse, Jakob
AU  - Rother, Carsten
AU  - Köthe, Ullrich
AU  - Maier-Hein, Lena
TI  - Uncertainty-aware performance assessment of optical imaging modalities with invertible neural networks.
JO  - International journal of computer assisted radiology and surgery
VL  - 14
IS  - 6
SN  - 1861-6429
CY  - Heidelberg [u.a.]
PB  - Springer
M1  - DKFZ-2019-00930
SP  - 997-1007
PY  - 2019
AB  - 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.
LB  - PUB:(DE-HGF)16
C6  - pmid:30903566
DO  - DOI:10.1007/s11548-019-01939-9
UR  - https://inrepo02.dkfz.de/record/143340
ER  -