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About

Jan Sellner is a researcher at the forefront of surgical computer vision, with a focus on hyperspectral imaging (HSI) and machine learning-based tissue analysis in intraoperative settings. His work addresses one of surgery's persistent challenges: the difficulty of visually distinguishing between tissue types during procedures, where misidentification can have serious clinical consequences. Sellner's most influential contribution, "Robust deep learning-based semantic organ segmentation in hyperspectral images" (2022, 84 citations), pioneered full-scene semantic segmentation using spectral imaging data rather than conventional RGB video, opening new pathways for context-aware and autonomous surgical robotics. Complementing this, his research on "spectral organ fingerprints" demonstrated that machine learning models can reliably classify tissues by their unique spectral signatures—work that has collectively attracted over 60 additional citations across porcine and clinical models. His 2023 study on semantic segmentation under geometric domain shifts further advances the robustness of these systems for real-world surgical deployment. Across his publication record, Sellner has established himself as a key contributor to the emerging field of hyperspectral surgical imaging, with research that bridges advanced optical sensing, deep learning, and practical clinical applicability—making his work essential reading for students interested in medical AI and computer-assisted surgery.