Ex vivoMueller polarimetric imaging of the uterine cervix: a first statistical evaluation

Abstract

Early detection through screening plays a major role in reducing the impact of cervical cancer on patients. When detected before the invasive stage, precancerous lesions can be eliminated with very limited surgery. Polarimetric imaging is a potential alternative to the standard screening methods currently used. In a previous proof-of-concept study, significant contrasts have been found in polarimetric images acquired for healthy and precancerous regions of excised cervical tissue. To quantify the ability of the technique to differentiate between healthy and precancerous tissue, polarimetric images of seventeen cervical conization specimens (cone-shaped or cylindrical wedges from the uterine cervix) are compared with results from histopathological diagnoses, which is considered to be the “gold standard.” The sensitivity and specificity of the technique are calculated for images acquired at wavelengths of 450, 550, and 600 nm, aiming to differentiate between high-grade cervical intraepithelial neoplasia (CIN 2-3) and healthy squamous epithelium. To do so, a sliding threshold for the scalar retardance parameter was used for the sample zones, as labeled after histological diagnosis. An optimized value of ∼83% is achieved for both sensitivity and specificity for images acquired at 450 nm and for a threshold scalar retardance value of 10.6 deg. This study paves the way for an application of polarimetry in the clinic.

In vivo imaging of uterine cervix with a Mueller polarimetric colposcope

“…Previous studies showed that the relevant polarimetric parameters for analyzing the uterine cervix microstructure were the Depolarization (Δ) and Retardance (R) 12, 14 . Depolarization enables one to evaluate the scattering properties of tissue while Retardance to quantify its anisotropy, generally due to the presence of collagen fibers, which are known to be strongly birefringent.…”

Mueller matrix approach for probing multifractality in the underlying anisotropic connective tissue

“…13,14 It may be pertinent to note, in this regard, that recent studies have also demonstrated that changes in tissue birefringence property (quantified from MM) are related to morphological changes in cervical precancers. 15,16 In our approach, we have therefore employed Born approximation-based Fourier domain preprocessing 4 on wavelength variation of the selected MM elements (those encode the linear birefringence information). Such preprocessing captures information on the statistically equivalent spatial RI fluctuations of the anisotropic layer.…”

“…MM encodes two basic anisotropy properties, namely, the retardance (birefringence) and the diattenuation (dichroism). 15,16 The former represents phase anisotropy (differential phase between orthogonal polarization), anisotropy in real part of the RI, and the latter represent amplitude anisotropy (differential attenuation via scattering and/or absorption between orthogonal polarization). 13,14 In this study, backscattering spectral Mueller matrices were recorded and subsequently decomposed to extract the linear retardance parameter.…”

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

“…In prior work a proof-of-concept study demonstrated significant contrast in Mueller polarimetric images acquired for healthy and pre-cancerous regions of excised cervical tissue [1]. To quantify the ability of this modality to differentiate between healthy and pre-cancerous tissue, polarimetric Mueller images of seventeen cervical specimens were compared to results from histopathology; excised tissue shown in Fig.…”

“…In this prior work an optimized value of 83% was achieved for both sensitivity and specificity for images acquired at 450nm and for a threshold scalar retardance value of 10.6. 1 In this work we aim to improve this detection performance by using a novel classification algorithms designed to compute quadratic classifiers from high-dimensional image data. This J-optimal channelized quadratic observer (J-CQO) is an algorithm that can incorporate all polarimetric, spectral, and local spatial measurements into the classifier.…”