Alma Eguizabal

Optical coherence tomography assessment of vessel wall degradation in thoracic aortic aneurysms

Abstract. Optical coherence tomography images of human thoracic aorta from aneurysms reveal elastin disorders and smooth muscle cell alterations when visualizing the media layer of the aortic wall. These disorders can be employed as indicators for wall degradation and, therefore, become a hallmark for diagnosis of risk of aneurysm under intraoperative conditions. Two approaches are followed to evaluate this risk: the analysis of the reflectivity decay along the penetration depth and the textural analysis of a two-dimensional spatial distribution of the aortic wall backscattering. Both techniques require preprocessing stages for the identification of the air–sample interface and for the segmentation of the media layer. Results show that the alterations in the media layer of the aortic wall are better highlighted when the textural approach is considered and also agree with a semiquantitative histopathological grading that assesses the degree of wall degradation. The correlation of the co-occurrence matrix attains a sensitivity of 0.906 and specificity of 0.864 when aneurysm automatic diagnosis is evaluated with a receiver operating characteristic curve.

Direct identification of breast cancer pathologies using blind separation of label-free localized reflectance measurements

Breast tumors are blindly identified using Principal (PCA) and Independent Component Analysis (ICA) of localized reflectance measurements. No assumption of a particular theoretical model for the reflectance needs to be made, while the resulting features are proven to have discriminative power of breast pathologies. Normal, benign and malignant breast tissue types in lumpectomy specimens were imaged ex vivo and a surgeon-guided calibration of the system is proposed to overcome the limitations of the blind analysis. A simple, fast and linear classifier has been proposed where no training information is required for the diagnosis. A set of 29 breast tissue specimens have been diagnosed with a sensitivity of 96% and specificity of 95% when discriminating benign from malignant pathologies. The proposed hybrid combination PCA-ICA enhanced diagnostic discrimination, providing tumor probability maps, and intermediate PCA parameters reflected tissue optical properties.

Development and integration of xerogel polymeric absorbance micro-filters into lab-on-chip systems

This work reports on the implementation of different absorption micro-filters based on a dye-doped hybrid organic-inorganic xerogel polymeric material synthesized by the sol-gel process. Microstructures containing eight different filter widths were fabricated in polydimethylsiloxane (PDMS), bonded to glass substrates and filled with the corresponding dye doped polymeric material by a soft lithography approach. The filtering capacity as a function of dye concentration and filter width was studied and revealed a linear dependence with both parameters, as expected according to the Beer-Lambert law. Zero passband transmittance values and relatively sharp stopband regions were achieved with all the filters, also showing rejection levels between -6 dB and -55 dB. Finally, such filters were monolithically integrated into a disposable fluorescence-based photonic lab-on-a-chip (PhLoC) approach. Calibration curves carried out with a model fluorophore target analyte showed an over two-fold increase in sensitivity and a thirty-fold decrease of the limit of detection (LOD) compared with the values recorded using the same PhLoC system but without the polymeric filter structure. The results presented herein clearly indicate the feasibility of these xerogel-based absorbance filtering structures for being applied as low-cost optical components that can be easily incorporated into disposable fluorescence-based photonic lab on a chip systems.

Fractal analysis of scatter imaging signatures to distinguish breast pathologies

Fractal analysis combined with a label-free scattering technique is proposed for describing the pathological architecture of tumors. Clinicians and pathologists are conventionally trained to classify abnormal features such as structural irregularities or high indices of mitosis. The potential of fractal analysis lies in the fact of being a morphometric measure of the irregular structures providing a measure of the object’s complexity and self-similarity. As cancer is characterized by disorder and irregularity in tissues, this measure could be related to tumor growth. Fractal analysis has been probed in the understanding of the tumor vasculature network. This work addresses the feasibility of applying fractal analysis to the scattering power map (as a physical modeling) and principal components (as a statistical modeling) provided by a localized reflectance spectroscopic system. Disorder, irregularity and cell size variation in tissue samples is translated into the scattering power and principal components magnitude and its fractal dimension is correlated with the pathologist assessment of the samples. The fractal dimension is computed applying the box-counting technique. Results show that fractal analysis of ex-vivo fresh tissue samples exhibits separated ranges of fractal dimension that could help classifier combining the fractal results with other morphological features. This contrast trend would help in the discrimination of tissues in the intraoperative context and may serve as a useful adjunct to surgeons.

Optical spectroscopic sensors: From the control of industrial processes to tumor delineation

Optical spectroscopy is a consolidated line of research with several translational opportunities in the industrial and clinical contexts. The reflected and transmitted light spectrum of gaseous, liquid and solid materials offers direct information about the identification and quantification of their components, its morphology, etc. Different fiber-optics and non-fiber optics systems acquire the spectrum depending on the application field. Supervised or blind analyses of the materials spectrum allow the separation of raw material, to know the presence and concentration of dangerous gases, the assessment on the correct recipes of textile dyes or the identification of tumors and cardiovascular pathologies.

Enhanced tumor contrast during breast lumpectomy provided by independent component analysis of localized reflectance measures

A spectral analysis technique to enhance tumor contrast during breast conserving surgery is proposed. A set of 29 surgically-excised breast tissues have been imaged in local reflectance geometry. Measures of broadband reflectance are directly analyzed using Principle Component Analysis (PCA), on a per sample basis, to extract areas of maximal spectral variation. A dynamic selection threshold has been applied to obtain the final number of principal components, accounting for inter-patient variability. A blind separation technique based on Independent Component Analysis (ICA) is then applied to extract diagnostically powerful results. ICA application reveals that the behavior of one independent component highly correlates with the pathologic diagnosis and it surpasses the contrast obtained using empirical models. Moreover, blind detection characteristics (no training, no comparisons with training reference data) and no need for parameterization makes the automated diagnosis simple and time efficient, favoring its translation to the clinical practice. Correlation coefficient with model-based results up to 0.91 has been achieved.

Identification of vessel wall anomalies in thoracic aortic aneurysms through optical coherence tomography and gradient-based strategies

Optical Coherence Tomography is a natural candidate for imaging biological structures just under tissue surface. Human thoracic aorta from aneurysms reveal elastin disorders and smooth muscle cell alterations when visualizing the media layer of the aortic wall, which is only some tens of microns in depth from surface. The resulting images require a suitable processing to enhance interesting disorder features and to use them as indicators for wall degradation, converting OCT into a hallmark for diagnosis of risk of aneurysm under intraoperative conditions. This work proposes gradient-based digital image processing approaches to conclude this risk. These techniques are believed to be useful in these applications as aortic wall disorders directly affect the refractive index of the tissue, having an effect on the gradient of the tissue reflectivity that conform the OCT image. Preliminary results show that the direction of the gradient contains information to estimate the tissue abnormality score. The detection of the edges of the OCT image is performed using the Canny algorithm. The edges delineate tissue disorders in the region of interest and isolate the abnormalities. These edges can be quantified to estimate a degradation score. Furthermore, the direction of the gradient seems to be a promising enhancement technique, as it detects areas of homogeneity in the region of interest. Automatic results from gradient-based strategies are finally compared to the histopathological global aortic score, which accounts for each risk factor presence and seriousness.

Linear classifier and textural analysis of optical scattering images for tumor classification during breast cancer extraction

Texture analysis of light scattering in tissue is proposed to obtain diagnostic information from breast cancer specimens. Light scattering measurements are minimally invasive, and allow the estimation of tissue morphology to guide the surgeon in resection surgeries. The usability of scatter signatures acquired with a micro-sampling reflectance spectral imaging system was improved utilizing an empirical approximation to the Mie theory to estimate the scattering power on a per-pixel basis. Co-occurrence analysis is then applied to the scattering power images to extract the textural features. A statistical analysis of the features demonstrated the suitability of the autocorrelation for the classification of notmalignant (normal epithelia and stroma, benign epithelia and stroma, inflammation), malignant (DCIS, IDC, ILC) and adipose tissue, since it reveals morphological information of tissue. Non-malignant tissue shows higher autocorrelation values while adipose tissue presents a very low autocorrelation on its scatter texture, being malignant the middle ground. Consequently, a fast linear classifier based on the consideration of just one straightforward feature is enough for providing relevant diagnostic information. A leave-one-out validation of the linear classifier on 29 samples with 48 regions of interest showed classification accuracies of 98.74% on adipose tissue, 82.67% on non-malignant tissue and 72.37% on malignant tissue, in comparison with the biopsy H and E gold standard. This demonstrates that autocorrelation analysis of scatter signatures is a very computationally efficient and automated approach to provide pathological information in real-time to guide surgeon during tissue resection.

Optical coherence tomography assessment of vessel wall degradation in aneurysmatic thoracic aortas

Optical coherence tomographic images of ascending thoracic human aortas from aneurysms exhibit disorders on the smooth muscle cell structure of the media layer of the aortic vessel as well as elastin degradation. Ex-vivo measurements of human samples provide results that correlate with pathologist diagnosis in aneurysmatic and control aortas. The observed disorders are studied as possible hallmarks for aneurysm diagnosis. To this end, the backscattering profile along the vessel thickness has been evaluated by fitting its decay against two different models, a third order polynomial fitting and an exponential fitting. The discontinuities present on the vessel wall on aneurysmatic aortas are slightly better identified with the exponential approach. Aneurysmatic aortic walls present uneven reflectivity decay when compared with healthy vessels. The fitting error has revealed as the most favorable indicator for aneurysm diagnosis as it provides a measure of how uniform is the decay along the vessel thickness.

Textural analysis of optical scattering for identification of cancer in breast surgical specimens

Textural analysis of tissue scattering images is proposed for healthy versus tumor discrimination. Scattering center density varies from normal to tumor tissues and this variation is translated into different textures in the scattering power map. Adipose tissue shows low autocorrelation values while tumor tissues present higher entropies than normal tissue. Consequently, a combination of autocorrelation and entropy values allows ready tissue discrimination by a supervised linear classifier. The proposed approach has been validated over a set of 29 breast tissue samples achieving a sensitivity of 73.59% and specificity of 82.40%.