You are here : Home > Targeting Cancer Detection & Identification of Microorganisms, CEA-Leti Develops Mid-Infrared, Spectral-Imaging Technique

Press release | News

Targeting Cancer Detection & Identification of Microorganisms, CEA-Leti Develops Mid-Infrared, Spectral-Imaging Technique


Presentations at Photonics West 2021 Show How Early-Stage Imaging System's Flexibility Can Be Applied Broadly in Medical Field


Published on 19 March 2021

GRENOBLE, France – March 18, 2021 – CEA-Leti scientists have developed a lensless, infrared spectral-imaging system for medical diagnostics. The first application is cancer detection in the tissue section and the second is the identification and discrimination of microorganisms, such as bacteria. Presented in two papers at the Photonics West 2021 Digital Forum, the label-free technology also could eliminate sample preparation in a reliable and user-friendly device that may foretoken automation of some diagnostics.

  • This new imaging tool allows quickly obtaining simultaneously morphological and biochemical information from a sample.



Caption: Six images at relevant wavelengths to differentiate tumor cells


  • The paper reported that by analyzing images from mice tissue using amide and DNA absorption bands, the team "achieved up to 94 percent of successful predictions of cancer cells with a population of 325 pixels corresponding to muscle tissues and 325 pixels corresponding to cancer tissues. This work may lead to the development of an imaging device that could be used for cancer diagnosis at hospitals."

Employing recent developments in photonics components, which allow using infrared light to detect abnormal tissues, mid-IR imaging can provide unequivocal information about the biochemical composition of human cells, said Grégoire Mathieu, lead author of the first paper. The combination of a set of lasers and lensless imaging with an uncooled bolometer matrix allows biochemical mapping over a wide field of view. The project showed that this experiment's setup coupled to machine learning algorithms (Random Forest, Neural Networks, K-means) can help to classify the biological cells in a fast and reproducible way.


Caption: Multispectral images of representative examples from the seven species of the database. Wavenumbers on top of each column are in cm-1.


  • For this proof of concept, a database containing 2,253 colonies belonging to eight different species and three  strains of S. epidermidis was acquired. The optical setup and machine-learning analysis allowed classifying all species with a correct identification rate (CIR) of at least 91 percent. 
  • The early-stage technology used in both studies was enabled in part by recent improvements in photonics components at CEA-Leti. The next steps are to perform a dedicated prototype with the relevant wavelengths and to demonstrate the performance of the system with real-life samples, such as human biopsies, and to create larger databases for each application. In addition, a start up is currently in incubation. 

Top page