{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T02:23:04Z","timestamp":1779416584822,"version":"3.53.1"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T00:00:00Z","timestamp":1759795200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T00:00:00Z","timestamp":1759795200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100019180","name":"HORIZON EUROPE European Research Council","doi-asserted-by":"publisher","award":["101002198"],"award-info":[{"award-number":["101002198"]}],"id":[{"id":"10.13039\/100019180","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s11548-025-03525-8","type":"journal-article","created":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T12:30:40Z","timestamp":1759840240000},"page":"665-675","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Neural illumination calibration for surgical workflow-optimized spectral imaging"],"prefix":"10.1007","volume":"21","author":[{"given":"Alexander","family":"Baumann","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leonardo","family":"Ayala","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"Studier-Fischer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jan","family":"Sellner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Berkin","family":"\u00d6zdemir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karl-Friedrich","family":"Kowalewski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Slobodan","family":"Ilic","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Silvia","family":"Seidlitz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lena","family":"Maier-Hein","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,7]]},"reference":[{"key":"3525_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102488","volume":"80","author":"S Seidlitz","year":"2022","unstructured":"Seidlitz S, Sellner J, Odenthal J, \u00d6zdemir B, Studier-Fischer A, Kn\u00f6dler S, Ayala L, Adler TJ, Kenngott HG, Tizabi M (2022) Robust deep learning-based semantic organ segmentation in hyperspectral images. Med Image Anal 80:102488","journal-title":"Med Image Anal"},{"key":"3525_CR2","doi-asserted-by":"crossref","unstructured":"Sellner J, Seidlitz S, Studier-Fischer A, Motta A, \u00d6zdemir B, M\u00fcller-Stich BP, Nickel F, Maier-Hein L (2023) Semantic segmentation of surgical hyperspectral images under geometric domain shifts. In: International Conference on Medical Image Computing and Computer- Assisted Intervention, pp. 618\u2013627 (2023). Springer","DOI":"10.1007\/978-3-031-43996-4_59"},{"issue":"4","key":"3525_CR3","doi-asserted-by":"publisher","first-page":"1330","DOI":"10.1109\/TBME.2020.3026683","volume":"68","author":"S Trajanovski","year":"2020","unstructured":"Trajanovski S, Shan C, Weijtmans PJ, Koning SGB, Ruers TJ (2020) Tongue tumor detection in hyperspectral images using deep learning semantic segmentation. IEEE Trans Biomed Eng 68(4):1330\u20131340","journal-title":"IEEE Trans Biomed Eng"},{"issue":"6","key":"3525_CR4","doi-asserted-by":"publisher","first-page":"756","DOI":"10.3390\/cancers11060756","volume":"11","author":"M Halicek","year":"2019","unstructured":"Halicek M, Fabelo H, Ortega S, Callico GM, Fei B (2019) In-vivo and ex-vivo tissue analysis through hyperspectral imaging techniques: revealing the invisible features of cancer. Cancers 11(6):756","journal-title":"Cancers"},{"issue":"6","key":"3525_CR5","doi-asserted-by":"publisher","first-page":"060503","DOI":"10.1117\/1.JBO.22.6.060503","volume":"22","author":"M Halicek","year":"2017","unstructured":"Halicek M, Lu G, Little JV, Wang X, Patel M, Griffith CC, El-Deiry MW, Chen AY, Fei B (2017) Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging. J Biomed Opt 22(6):060503\u2013060503","journal-title":"J Biomed Opt"},{"key":"3525_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101699","volume":"63","author":"NT Clancy","year":"2020","unstructured":"Clancy NT, Jones G, Maier-Hein L, Elson DS, Stoyanov D (2020) Surgical spectral imaging. Med Image Anal 63:101699","journal-title":"Med Image Anal"},{"key":"3525_CR7","doi-asserted-by":"crossref","unstructured":"Ayala LA, Wirkert SJ, Gr\u00f6hl J, Herrera MA, Hernandez-Aguilera A, Vemuri A, Santos E, Maier-Hein L (2019) Live monitoring of haemodynamic changes with multispectral image analysis. In: OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging: Second International Workshop, OR 2.0 2019, and Second International Workshop, MLCN 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings 2, pp. 38\u201346. Springer","DOI":"10.1007\/978-3-030-32695-1_5"},{"issue":"10","key":"3525_CR8","doi-asserted-by":"publisher","first-page":"6778","DOI":"10.1126\/sciadv.add6778","volume":"9","author":"L Ayala","year":"2023","unstructured":"Ayala L, Adler TJ, Seidlitz S, Wirkert S, Engels C, Seitel A, Sellner J, Aksenov A, Bodenbach M, Bader P (2023) Spectral imaging enables contrast agent-free real-time ischemia monitoring in laparoscopic surgery. Sci Adv 9(10):6778","journal-title":"Sci Adv"},{"key":"3525_CR9","doi-asserted-by":"crossref","unstructured":"Kulcke A, Holmer A, Wahl P, Siemers F, Wild T, Daeschlein G (2018) A compact hyperspectral camera for measurement of perfusion parameters in medicine. Biomedical Engineering\/Biomedizinische Technik 63(5), 519\u2013527","DOI":"10.1515\/bmt-2017-0145"},{"issue":"11","key":"3525_CR10","doi-asserted-by":"publisher","first-page":"2064","DOI":"10.1088\/0967-3334\/37\/11\/2064","volume":"37","author":"A Holmer","year":"2016","unstructured":"Holmer A, Tetschke F, Marotz J, Malberg H, Markgraf W, Thiele C, Kulcke A (2016) Oxygenation and perfusion monitoring with a hyperspectral camera system for chemical based tissue analysis of skin and organs. Physiol Meas 37(11):2064","journal-title":"Physiol Meas"},{"key":"3525_CR11","doi-asserted-by":"crossref","unstructured":"Wirkert SJ, Vemuri AS, Kenngott HG, Moccia S, G\u00f6tz M, Mayer BF, Maier-Hein KH, Elson DS, Maier-Hein L (2017) Physiological parameter estimation from multispectral images unleashed. In: Medical Image Computing and Computer Assisted Intervention- MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part III 20, pp. 134\u2013141. Springer","DOI":"10.1007\/978-3-319-66179-7_16"},{"issue":"9","key":"3525_CR12","doi-asserted-by":"publisher","first-page":"201800455","DOI":"10.1002\/jbio.201800455","volume":"12","author":"J Shapey","year":"2019","unstructured":"Shapey J, Xie Y, Nabavi E, Bradford R, Saeed SR, Ourselin S, Vercauteren T (2019) Intraoperative multispectral and hyperspectral label-free imaging: a systematic review of in vivo clinical studies. J Biophotonics 12(9):201800455","journal-title":"J Biophotonics"},{"issue":"29","key":"3525_CR13","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6463\/abfbf6","volume":"54","author":"M Ebner","year":"2021","unstructured":"Ebner M, Nabavi E, Shapey J, Xie Y, Liebmann F, Spirig JM, Hoch A, Farshad M, Saeed SR, Bradford R (2021) Intraoperative hyperspectral label-free imaging: from system design to first-in-patient translation. J Phys D Appl Phys 54(29):294003","journal-title":"J Phys D Appl Phys"},{"issue":"1","key":"3525_CR14","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1038\/s41597-023-02315-8","volume":"10","author":"A Studier-Fischer","year":"2023","unstructured":"Studier-Fischer A, Seidlitz S, Sellner J, Bressan M, \u00d6zdemir B, Ayala L, Odenthal J, Knoedler S, Kowalewski K-F, Haney CM (2023) Heiporspectral-the heidelberg porcine hyperspectral imaging dataset of 20 physiological organs. Sci Data 10(1):414","journal-title":"Sci Data"},{"issue":"4","key":"3525_CR15","doi-asserted-by":"publisher","first-page":"046001","DOI":"10.1117\/1.JMI.10.4.046001","volume":"10","author":"A Bahl","year":"2023","unstructured":"Bahl A, Horgan CC, Janatka M, MacCormac OJ, Noonan P, Xie Y, Qiu J, Cavalcanti N, F\u00fcrnstahl P, Ebner M (2023) Synthetic white balancing for intra-operative hyperspectral imaging. J Med Imag 10(4):046001\u2013046001","journal-title":"J Med Imag"},{"issue":"1","key":"3525_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/0016-0032(80)90058-7","volume":"310","author":"G Buchsbaum","year":"1980","unstructured":"Buchsbaum G (1980) A spatial processor model for object colour perception. J Franklin Inst 310(1):1\u201326","journal-title":"J Franklin Inst"},{"issue":"6","key":"3525_CR17","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1038\/scientificamerican1277-108","volume":"237","author":"EH Land","year":"1977","unstructured":"Land EH (1977) The retinex theory of color vision. Sci Am 237(6):108\u2013129","journal-title":"Sci Am"},{"issue":"9","key":"3525_CR18","doi-asserted-by":"publisher","first-page":"2207","DOI":"10.1109\/TIP.2007.901808","volume":"16","author":"J Weijer","year":"2007","unstructured":"Weijer J, Gevers T, Gijsenij A (2007) Edge-based color constancy. IEEE Trans Image Process 16(9):2207\u20132214","journal-title":"IEEE Trans Image Process"},{"key":"3525_CR19","doi-asserted-by":"publisher","first-page":"1117","DOI":"10.1007\/s11548-020-02195-y","volume":"15","author":"L Ayala","year":"2020","unstructured":"Ayala L, Seidlitz S, Vemuri A, Wirkert SJ, Kirchner T, Adler TJ, Engels C, Teber D, Maier-Hein L (2020) Light source calibration for multispectral imaging in surgery. Int J Comput Assist Radiol Surg 15:1117\u20131125","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"3525_CR20","doi-asserted-by":"crossref","unstructured":"Barron JT (2015) Convolutional color constancy. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 379\u2013387","DOI":"10.1109\/ICCV.2015.51"},{"key":"3525_CR21","doi-asserted-by":"crossref","unstructured":"Hu Y, Wang B, Lin S (2017) Fc4: Fully convolutional color constancy with confidence-weighted pooling. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4085\u20134094","DOI":"10.1109\/CVPR.2017.43"},{"key":"3525_CR22","doi-asserted-by":"crossref","unstructured":"Glatt O, Ater Y, Kim W-S, Werman S, Berby O, Zini Y, Zelinger S, Lee S, Choi H, Soloveichik E (2024) Beyond rgb: a real world dataset for multispectral imaging in mobile devices. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 4344\u20134354","DOI":"10.1109\/WACV57701.2024.00429"},{"issue":"6","key":"3525_CR23","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1016\/j.surge.2023.05.004","volume":"21","author":"N Sharma","year":"2023","unstructured":"Sharma N, Heer A, Su L (2023) A timeline of surgical lighting-is automated lighting the future? The Surgeon 21(6):369\u2013374","journal-title":"The Surgeon"},{"key":"3525_CR24","doi-asserted-by":"crossref","unstructured":"Sidorov O (2019) Conditional gans for multi-illuminant color constancy: Revolution or yet another approach? In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 0\u20130","DOI":"10.1109\/CVPRW.2019.00225"},{"key":"3525_CR25","doi-asserted-by":"publisher","first-page":"8964","DOI":"10.1109\/ACCESS.2018.2808502","volume":"6","author":"MA Hussain","year":"2018","unstructured":"Hussain MA, Akbari AS (2018) Color constancy algorithm for mixed-illuminant scene images. IEEE Access 6:8964\u20138976","journal-title":"IEEE Access"},{"key":"3525_CR26","doi-asserted-by":"crossref","unstructured":"Das P, Liu Y, Karaoglu S, Gevers T (2021) Generative models for multi-illumination color constancy. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1194\u20131203","DOI":"10.1109\/ICCVW54120.2021.00139"},{"key":"3525_CR27","doi-asserted-by":"crossref","unstructured":"Li Y, Fu Q, Heidrich W (2021) Multispectral illumination estimation using deep unrolling network. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2672\u20132681","DOI":"10.1109\/ICCV48922.2021.00267"},{"key":"3525_CR28","doi-asserted-by":"crossref","unstructured":"Baumann A, Ayala L, Studier-Fischer A, Sellner J, \u00d6zdemir B, Kowalewski K-F, Ilic S, Seidlitz S, Maier-Hein L (2024) Deep intra-operative illumination calibration of hyperspectral cameras. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 120\u2013131. Springer","DOI":"10.1007\/978-3-031-72089-5_12"},{"issue":"9","key":"3525_CR29","doi-asserted-by":"publisher","first-page":"2475","DOI":"10.1109\/TIP.2011.2118224","volume":"20","author":"A Gijsenij","year":"2011","unstructured":"Gijsenij A, Gevers T, Weijer J (2011) Computational color constancy: survey and experiments. IEEE Trans Image Process 20(9):2475\u20132489","journal-title":"IEEE Trans Image Process"},{"issue":"9","key":"3525_CR30","doi-asserted-by":"publisher","first-page":"972","DOI":"10.1109\/TIP.2002.802531","volume":"11","author":"K Barnard","year":"2002","unstructured":"Barnard K, Cardei V, Funt B (2002) A comparison of computational color constancy algorithms. i: Methodology and experiments with synthesized data. IEEE Trans Image Process 11(9):972\u2013984","journal-title":"IEEE Trans Image Process"},{"issue":"9","key":"3525_CR31","doi-asserted-by":"publisher","first-page":"985","DOI":"10.1109\/TIP.2002.802529","volume":"11","author":"K Barnard","year":"2002","unstructured":"Barnard K, Martin L, Coath A, Funt B (2002) A comparison of computational color constancy algorithms. ii. experiments with image data. IEEE Trans Image Process 11(9):985\u2013996","journal-title":"IEEE Trans Image Process"},{"key":"3525_CR32","unstructured":"Sellner J, Studier-Fischer A, Qasim AB, Seidlitz S, Schreck N, Tizabi M, Wiesenfarth M, Kopp-Schneider A, Kn\u00f6dler S, Haney CM et al (2024) Xeno-learning: knowledge transfer across species in deep learning-based spectral image analysis. arXiv preprint arXiv:2410.19789"},{"key":"3525_CR33","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"3525_CR34","doi-asserted-by":"crossref","unstructured":"Maier-Hein L, Reinke A, Godau P, Tizabi MD, Buettner F, Christodoulou E, Glocker B, Isensee F, Kleesiek J, Kozubek M et al (2024) Metrics reloaded: recommendations for image analysis validation. Nature methods, 1\u201318","DOI":"10.1038\/s41592-023-02151-z"},{"issue":"1","key":"3525_CR35","doi-asserted-by":"publisher","first-page":"2369","DOI":"10.1038\/s41598-021-82017-6","volume":"11","author":"M Wiesenfarth","year":"2021","unstructured":"Wiesenfarth M, Reinke A, Landman BA, Eisenmann M, Saiz LA, Cardoso MJ, Maier-Hein L, Kopp-Schneider A (2021) Methods and open-source toolkit for analyzing and visualizing challenge results. Sci Rep 11(1):2369","journal-title":"Sci Rep"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-025-03525-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11548-025-03525-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-025-03525-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T01:30:31Z","timestamp":1779413431000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11548-025-03525-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,7]]},"references-count":35,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["3525"],"URL":"https:\/\/doi.org\/10.1007\/s11548-025-03525-8","relation":{},"ISSN":["1861-6429"],"issn-type":[{"value":"1861-6429","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,7]]},"assertion":[{"value":"4 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 October 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 December 2025","order":5,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Update","order":6,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Missing supplementary material has been updated","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The ethics for the animal experiments was granted by the Committee on Animal Experimentation of the Baden-W\u00fcrttemberg Regional Council in Karlsruhe, Germany (G-261\/19, G-262\/19, G-62\/23). The HSI human data were obtained during the SPACE trial (SPectrAl Characterization of organs and tissuEs during surgery) at Heidelberg University Hospital, with approval from the Ethics Committee of the Medical Faculty of Heidelberg University, Germany (S-459\/2020). The trial adhered to the ethical principles of the Declaration of Helsinki and the principles of Good Clinical Practice.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}}]}}