Comparison between Cylindrical, Trigonal, and General Symmetry Models for the Analysis of Polarization-Dependent Second Harmonic Generation Measurements Acquired from Collagen-Rich Equine Pericardium Samples

Mari, Meropi; Tsafas, Vassilis; Staraki, Despina; Fotakis, C.; Filippidis, George

doi:10.3390/photonics9040254

Cited by 1 publication

(2 citation statements)

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“…Collagen structural parameters modeling is usually done by performing an approximation of a cylindrical symmetry for the collagen fiber [1,2]. Such approximation has shown its limitations in numerous situations [4]. To overcome these limitations, a trigonal symmetry approximation can be used instead of a cylindrical symmetry [4].…”

Section: Introductionmentioning

confidence: 99%

“…Such approximation has shown its limitations in numerous situations [4]. To overcome these limitations, a trigonal symmetry approximation can be used instead of a cylindrical symmetry [4]. In any cases, assuming a cylindrical or a trigonal symmetry is a case-dependent procedure which is difficult to generalize to a systematic and generic study of collagen alteration during tumorigenesis.…”

Section: Introductionmentioning

confidence: 99%

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

Baskiotis

Assainova²,

Saab³

et al. 2023

Polarized Light and Optical Angular Momentum for Biomedical Diagnostics 2023

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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.

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