TY  - JOUR
AU  - Baumann, Alexander
AU  - Ayala, Leonardo
AU  - Studier-Fischer, Alexander
AU  - Sellner, Jan
AU  - Özdemir, Berkin
AU  - Kowalewski, Karl-Friedrich
AU  - Ilic, Slobodan
AU  - Seidlitz, Silvia
AU  - Maier-Hein, Lena
TI  - Neural illumination calibration for surgical workflow-optimized spectral imaging.
JO  - International journal of computer assisted radiology and surgery
VL  - nn
SN  - 1861-6410
CY  - Heidelberg [u.a.]
PB  - Springer
M1  - DKFZ-2025-02056
SP  - nn
PY  - 2025
N1  - #EA:E130#LA:E130# / epub
AB  - 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.
KW  - Deep Learning (Other)
KW  - Hyperspectral Imaging (Other)
KW  - Illumination Calibration (Other)
KW  - Intra-Operative Imaging (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:41055831
DO  - DOI:10.1007/s11548-025-03525-8
UR  - https://inrepo02.dkfz.de/record/305208
ER  -