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However, surgical HSI datasets are scarce, hindering the development of robust data-driven algorithms. The purpose of this work was to address this critical bottleneck with a novel approach to knowledge transfer across modalities.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods<\/jats:title>\n            <jats:p>We propose the use of generative modeling to leverage imaging data across optical imaging modalities. The core of the method is a latent diffusion model (LDM) capable of converting a semantic segmentation mask obtained from any modality into a realistic hyperspectral image, such that geometry information can be learned across modalities. The value of the approach was assessed both qualitatively and quantitatively using surgical scene segmentation as a downstream task.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>Our study with more than 13,000 hyperspectral images, partially annotated with a total of 37 tissue and object classes, suggests that LDMs are well-suited for the synthesis of realistic high-resolution hyperspectral images even when trained on few samples or applied to annotations from different modalities and geometric out-of-distribution annotations. Using our approach for generative augmentation yielded a performance boost of up to 35% in the Dice similarity coefficient for the task of semantic hyperspectral image segmentation.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>As our method is capable of augmenting HSI datasets in a manner agnostic to the modality of the leveraged data, it could serve as a blueprint for addressing the data bottleneck encountered for novel imaging modalities.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1007\/s11548-025-03364-7","type":"journal-article","created":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T19:11:55Z","timestamp":1745521915000},"page":"1205-1213","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Semantic hyperspectral image synthesis for cross-modality knowledge transfer in surgical data science"],"prefix":"10.1007","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9135-745X","authenticated-orcid":false,"given":"Viet","family":"Tran Ba","sequence":"first","affiliation":[]},{"given":"Marco","family":"H\u00fcbner","sequence":"additional","affiliation":[]},{"given":"Ahmad","family":"Bin Qasim","sequence":"additional","affiliation":[]},{"given":"Maike","family":"Rees","sequence":"additional","affiliation":[]},{"given":"Jan","family":"Sellner","sequence":"additional","affiliation":[]},{"given":"Silvia","family":"Seidlitz","sequence":"additional","affiliation":[]},{"given":"Evangelia","family":"Christodoulou","sequence":"additional","affiliation":[]},{"given":"Berkin","family":"\u00d6zdemir","sequence":"additional","affiliation":[]},{"given":"Alexander","family":"Studier-Fischer","sequence":"additional","affiliation":[]},{"given":"Felix","family":"Nickel","sequence":"additional","affiliation":[]},{"given":"Leonardo","family":"Ayala","sequence":"additional","affiliation":[]},{"given":"Lena","family":"Maier-Hein","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,24]]},"reference":[{"key":"3364_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, Wagner M, Nickel F, M\u00fcller-Stich BP, Maier-Hein L (2022) Robust deep learning-based semantic organ segmentation in hyperspectral images. 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