Tasked-based quantification of measurement utility for ex vivo multi-spectral Mueller polarimetry of the uterine cervix

Kupinski, Meredith; Rehbinder, Jean; Haddad, Huda; Deby, Stanislas; Vizet, Jérémy; Teig, Benjamin; Nazac, André; Pierangelo, Angelo; Moreau, F.; Novikova, Tatiana

doi:10.1117/12.2286837

Cited by 4 publications

(1 citation statement)

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“…Various imaging techniques, primarily based on transmission, absorption, reflection, spontaneous emission and scattering in light‐tissue interactions, have shown to be responsive to a number of subtle alterations in various diseased tissue. Mueller matrix (MM) polarimetric imaging based on the acquisition of 16 images has shown tremendous potential in precancer detection since the last decade and is best suited for a complete characterization of the biological specimen . These MM images are further processed to extract the essential optical parameters, namely, diattenuation, retardation and depolarization through Lu‐Chipman decomposition method .…”

Section: Introductionmentioning

confidence: 99%

Mapping of retardance, diattenuation and polarizance vector on Poincare sphere for diagnosis and classification of cervical precancer

Zaffar

Pradhan

2020

Journal of Biophotonics

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The mapping of diattenuation, polarizance and retardance vector (normalized Stokes vector) on Poincare sphere, evaluated from Mueller matrix of optically anisotropic stromal region of cervical tissues, is presented for cervical precancer detection and its staging. This reveals that the changes in the polarization states shown by these normalized Stokes vectors corresponds to the degradation of linearly arranged collagen fibers, breakage of the collagen cross links in the stromal region and change in the density of scattering sites when cervical cancer evolves. The distinct nature of real and imaginary parts of the refractive index for linear, linear-45 and circular polarization from the optically anisotropic stromal region underscore the various polarization structures of the connective tissue region which get modified during the pathological changes. It has been found that versatility of these vectors for normal and precancerous cervical tissue of various grades may be utilized as a key distinction for qualitative staging of cervical precancer tissue. Quantitative classification of precancerous stages of cervical precancer has been determined with 95%-100% sensitivity and 93%-100% specificity through the evaluation of linear and circular diattenuation, linear polarizance and linear birefringence from the components of the respective vectors. K E Y W O R D S cervical cancer, diattenuation vector, Mueller matrix, Poincare sphere, polarizance vector, retardance vector, stromal region

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Section: Introductionmentioning

confidence: 99%

Mapping of retardance, diattenuation and polarizance vector on Poincare sphere for diagnosis and classification of cervical precancer

Zaffar

Pradhan

2020

Journal of Biophotonics

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Mueller Polarimetry of Brain Tissues

Novikova

Pierangelo

Schucht

et al. 2022

Polarized Light in Biomedical Imaging and Sensing

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Polarimetric imaging for cervical pre-cancer screening aided by machine learning: ex vivo studies

Robinson,

Hoong,

Kleijn

et al. 2023

J. Biomed. Opt.

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.SignificanceWide-field imaging Mueller polarimetry is an optical imaging technique that has great potential to become a reliable, fast, non-contact in vivo imaging modality for the early detection of, e.g., deceases and tissue structural malformations, such as cervical intraepithelial neoplasia, in both clinical and low-resource settings. On the other hand, machine learning methods have established themselves as a superior solution in image classification and regression tasks. We combine Mueller polarimetry and machine learning, critically assess the data/classification pipeline, investigate the bias arising from training strategies, and demonstrate how higher levels of detection accuracy can be achieved.AimWe aim to automate/assist with diagnostic segmentation of polarimetric images of uterine cervix specimens.ApproachA comprehensive capture-to-classification pipeline is developed in house. Specimens are acquired and measured with imaging Mueller polarimeter and undergo histopathological classification. Subsequently, a labeled dataset is created within tagged regions of either healthy or neoplastic cervical tissues. Several machine learning methods are trained utilizing different training-test-set-split strategies, and their corresponding accuracies are compared.ResultsOur results include robust measurements of model performance with two approaches: a 90:10 training–test-set-split and leave-one-out cross-validation. By comparing the classifier’s accuracy directly with the ground truth obtained during histology analysis, we demonstrate how conventionally used shuffled split leads to an over-estimate of true classifier performance ( 0.964 ± 0.00 ) . The leave-one-out cross-validation, however, leads to more accurate performance ( 0.812 ± 0.21 ) with respect to newly obtained samples that were not used to train the models.ConclusionsCombination of Mueller polarimetry and machine learning is a powerful tool for the task of screening for pre-cancerous conditions in cervical tissue sections. Nevertheless, there is a inherent bias with conventional processes that can be addressed using more conservative classifier training approaches. This results in overall improvements of the sensitivity and specificity of the developed techniques for “unseen” images.

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Tasked-based quantification of measurement utility for ex vivo multi-spectral Mueller polarimetry of the uterine cervix

Cited by 4 publications

References 4 publications

Mapping of retardance, diattenuation and polarizance vector on Poincare sphere for diagnosis and classification of cervical precancer

Mapping of retardance, diattenuation and polarizance vector on Poincare sphere for diagnosis and classification of cervical precancer

Mueller Polarimetry of Brain Tissues

Polarimetric imaging for cervical pre-cancer screening aided by machine learning: ex vivo studies

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