Information extraction in polarization-resolved second harmonic generation microscopy images of human tissues for automatic cancer diagnosis

Catherine Baskiotis; Olga Assainova; Nadine Abdallah Saab; Marwa El Bouz; Ayman Alfalou; Christian Brosseau

doi:10.1117/12.2649370

15 March 2023 Information extraction in polarization-resolved second harmonic generation microscopy images of human tissues for automatic cancer diagnosis

Catherine Baskiotis, Olga Assainova, Nadine Abdallah Saab, Marwa El Bouz, Ayman Alfalou, Christian Brosseau

Author Affiliations +

Proceedings Volume 12382, Polarized Light and Optical Angular Momentum for Biomedical Diagnostics 2023; 1238206 (2023) https://doi.org/10.1117/12.2649370
Event: SPIE BiOS, 2023, San Francisco, California, United States

Abstract

Polarization-resolved extension of Second Harmonic Generation microscopy (PSHG) exhibits proven efficiency in cancer diagnosis. Contrary to the case of white light microscopy, PSHG can reveal small structural collagen changes, during tumorigenesis, for a broad range of organs such as breast, thyroid, lung, pancreas, and ovary. However, despite its effectiveness for cancer diagnosis, PSHG is not yet fully exploited. One way of improvement consists in taking better advantage of polarization-resolved measurements which are performed by acquiring multiple images (usually between three to 20) of the same sample under different input beam polarization conditions. Each image of the resulting stacked raw images set can contain relevant information not found in the other images of the set. In the literature, information extraction from stacked raw images is performed using methods such as averaging of all images, collagen structural parameters modeling or PSHG polarimetric parameters extraction. If the two latter methods provide a richer information than the first one, they may, however, suffer from a loss of information from the stacked raw images. To examine this potential loss of information, AI methods can be used for extracting information from the stacked raw images. Using recently available images of the public SHG-TIFF database, dealing with breast and thyroid PSHG measurements of both normal and tumor tissues, we test available AI methods for information extraction and benchmark these methods to the state-of-the-art, in terms of automatic cancer diagnosis efficiency.

Conference Presentation

Citation Download Citation

Catherine Baskiotis, Olga Assainova, Nadine Abdallah Saab, Marwa El Bouz, Ayman Alfalou, and Christian Brosseau "Information extraction in polarization-resolved second harmonic generation microscopy images of human tissues for automatic cancer diagnosis", Proc. SPIE 12382, Polarized Light and Optical Angular Momentum for Biomedical Diagnostics 2023, 1238206 (15 March 2023); https://doi.org/10.1117/12.2649370

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