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Background:
Glioma surgery aims to maximize tumor resection while preserving healthy brain tissue and associated neurological functions. In this context, the CREATIS laboratory is developing optical spectroscopy approaches combining fluorescence, diffuse reflectance, and machine learning to characterize brain tissue and provide intraoperative decision support.
However, fluorescence measured in biological tissues is strongly affected by their absorption and scattering properties and may therefore differ substantially from the intrinsic fluorescence associated with tissue fluorophores [2,3]. In particular, absorption by oxy- and deoxyhemoglobin can alter both the shape and intensity of measured fluorescence spectra, potentially masking relevant spectral signatures [4]. Several approaches have therefore been proposed to correct these attenuation effects and improve the recovery of intrinsic fluorescence [1–4].
Objectives and Tasks:
The intern will develop a fluorescence correction module designed for integration into an existing spectral preprocessing pipeline.
The internship will include:
• Literature review of methods for correcting absorption and scattering effects in tissue fluorescence, with particular attention to oxy- and deoxyhemoglobin;
• Design and implementation of a correction method adapted to the experimental data available in the laboratory;
• Quantitative and qualitative validation of the correction using simulated and/or experimental data, including an assessment of its impact on downstream analyses;
• Integration of the method as a documented, tested, and reusable Python software module within the existing preprocessing pipeline.
The methodological approach will intentionally remain open and will be part of the research work. Several strategies may be investigated, including analytical correction methods, joint use of fluorescence and diffuse reflectance measurements, Monte Carlo simulations, machine learning or Deep Learning, Physics-Informed Neural Networks (PINNs), or hybrid approaches combining physical models and learning-based methods.
Candidate Profile:
We are looking for a Master’s student or final-year engineering student in signal processing, computer science/artificial intelligence, physics, photonics, biomedical engineering, or a related field.
Good proficiency in Python is expected. Previous knowledge of optics, numerical simulation, signal processing, machine learning, or Deep Learning would be an advantage.
This internship is particularly suited to candidates interested in working at the interface between physical modeling, artificial intelligence, and biomedical applications.
Application:
Please send a CV and cover letter highlighting relevant previous experience to:
hermine.quardon@creatis.insa-lyon.fr, cedric.ray-garreau@univ-lyon1.fr and
arthur.gautheron@creatis.insa-lyon.fr
Selected References:
[1] Bradley RS, Thorniley MS. A review of attenuation correction techniques for tissue
fluorescence. Journal of the Royal Society Interface. 2006. doi: 10.1098/rsif.2005.0066
[2] Müller MG, Georgakoudi I, Zhang Q, Wu J, Feld MS. Intrinsic fluorescence spectroscopy
in turbid media: disentangling effects of scattering and absorption. Applied Optics. 2001. doi:
10.1364/AO.40.004633
[3] Gardner CM, Jacques SL, Welch AJ. Fluorescence spectroscopy of tissue: recovery of
intrinsic fluorescence from measured fluorescence. Applied Optics. 1996. doi:
10.1364/AO.35.001780
[4] Le VND, Patterson MS, Farrell TJ, Hayward JE, Fang Q. Experimental recovery of
intrinsic fluorescence and fluorophore concentration in the presence of hemoglobin: spectral
effect of scattering and absorption on fluorescence. Journal of Biomedical Optics. 2015. doi:
10.1117/1.JBO.20.12.127003