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In this paper, we propose a methodology for extracting molecular tumor biomarkers from hyperspectral imaging (HSI), an emerging technology for intraoperative tissue assessment. To achieve this, we employ spectral unmixing, allowing to decompose the spectral signals recorded by the HSI camera into their constituent molecular components. Traditional unmixing approaches are based on physical models that establish a relationship between tissue molecules and the recorded spectra. However, these methods commonly assume a linear relationship between the spectra and molecular content, which does not capture the whole complexity of light-matter interaction. To address this limitation, we introduce a novel unmixing procedure that allows to take into account non-linear optical effects while preserving the computational benefits of linear spectral unmixing. We validate our methodology on an in-vivo brain tissue HSI dataset and demonstrate that the extracted molecular information leads to superior classification performance.

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Redefining Spectral Unmixing for In-Vivo Brain Tissue Analysis from Hyperspectral Imaging

  • 会议论文

  • 发表日期：

    2026年

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  • Martin Hartenberger,
  • Huzeyfe Ayaz,
  • Fatih Ozlugedik,
  • Charly Caredda,
  • Luca Giannoni,
  • Frédéric Lange,
  • Laurin Lux,
  • Jonas Weidner,
  • Alex Berger,
  • Florian Kofler,
  • Martin Menten,
  • Bruno Montcel,
  • Ilias Tachtsidis,
  • Daniel Rueckert,
  • Ivan Ezhov

摘要

In this paper, we propose a methodology for extracting molecular tumor biomarkers from hyperspectral imaging (HSI), an emerging technology for intraoperative tissue assessment. To achieve this, we employ spectral unmixing, allowing to decompose the spectral signals recorded by the HSI camera into their constituent molecular components. Traditional unmixing approaches are based on physical models that establish a relationship between tissue molecules and the recorded spectra. However, these methods commonly assume a linear relationship between the spectra and molecular content, which does not capture the whole complexity of light-matter interaction. To address this limitation, we introduce a novel unmixing procedure that allows to take into account non-linear optical effects while preserving the computational benefits of linear spectral unmixing. We validate our methodology on an in-vivo brain tissue HSI dataset and demonstrate that the extracted molecular information leads to superior classification performance.