Morphological analysis of optical coherence tomography images for automated classification of gastrointestinal tissues

The impact of digestive diseases, which include disorders affecting the oropharynx and alimentary canal, ranges from the inconvenience of a transient diarrhoea to dreaded conditions such as pancreatic cancer, which are usually fatal. Currently, the major limitation for the diagnosis of such diseases is sampling error because, even in the cases of rigorous adherence to biopsy protocols, only a tiny fraction of the surface of the involved gastrointestinal tract is sampled. Optical coherence tomography (OCT), which is an interferometric imaging technique for the minimally invasive measurement of biological samples, could decrease sampling error, increase yield, and even eliminate the need for tissue sampling provided that an automated, quick and reproducible tissue classification system is developed. Segmentation and quantification of ophthalmologic pathologies using OCT traditionally rely on the extraction of thickness and size measures from the OCT images, but layers are often not observed in nonopthalmic OCT imaging. Distinct mathematical methods, namely Principal Component Analysis (PCA) and textural analyses including both spatial textural analysis derived from the two-dimensional discrete Fourier transform (DFT) and statistical texture analysis obtained independently from center-symmetric autocorrelation (CSAC) and spatial grey-level dependency matrices (SGLDM), have been previously reported to overcome this problem. We propose an alternative approach consisting of a region segmentation according to the intensity variation along the vertical axis and a pure statistical technique for feature quantification, i.e. morphological analysis. Qualitative and quantitative comparisons with traditional approaches are accomplished in the discrimination of freshly-excised specimens of gastrointestinal tissues to exhibit the feasibility of the proposed method for computer-aided diagnosis (CAD) in the clinical setting

Correlation of p16INK4A Expression and HPV Copy Number with Cellular FTIR Spectroscopic Signatures of Cervical Cancer Cells

Cervical cancer, a potentially preventable disease, has its main aetiology in infection by high risk human papillomavirus (HR-HPV). Approaches to improving cervical cancer screening and diagnostic methodologies include molecular biological analysis, targeting of biomarker proteins, but also exploration and implementation of new techniques such as vibrational spectroscopy. This study correlates the biomarker protein p16INK4A expression levels dependent on HPV copy number with the infrared absorption spectral signatures of the cervical cancer cell lines, HPV negative C33A, HPV-16 positive SiHa and CaSki and HPV-18 positive HeLa. Confocal fluorescence microscopy demonstrated that p16INK4A is expressed in all investigated cell lines in both nuclear and cytoplasmic regions, although predominantly in the cytoplasm. Flow cytometry was used to quantify the p16INK4A expression levels and demonstrated a correlation, albeit nonlinear, between the reported number of integrated HPV copies and p16INK4A expression levels. CaSki cells were found to have the highest level of expression, HeLa intermediate levels, and SiHa and C33A the lowest levels. FTIR spectra revealed differences in nucleic acid, lipid and protein signatures between the cell lines with varying HPV copy number. Peak intensities exhibited increasing tendency in nucleic acid levels and decreasing tendency in lipid levels with increasing HPV copy number, and although they were found to be nonlinearly correlated with the HPV copy number, their dependence on p16INK4A levels was found to be close to linear. Principal Component Analysis (PCA) of the Infrared absorption spectra revealed differences between nuclear and cytoplasmic spectroscopic signatures for all cell lines, and furthermore clearly differentiated the groups of spectra representing each cell line. Finally, Partial Least Squares (PLS) analysis was employed to construct a model which can predict the p16INK4A expression level based on a spectral fingerprint of a cell line, demonstrating the diagnostic potential of spectroscopic techniques

Towards automated classification of clinical optimal coherence tomography (OCT) data obtained from dense tissues

Cervical cancer can be prevented if its precursors are recognised. Those lesions that justify preventive treatment are currently identified using methods that suffer from delayed results, false positives and subjective judgement. Optical coherence tomography (OCT) is a novel imaging modality that provides high-resolution backscattering data similar to ultrasonography. It could potentially provide in vivo and real-time imaging from within the entire cervical epithelium, where cervical cancer predominantly develops. In this study, we used a bench top OCT system with a 1310 nm light source. It employs fibre optics and operates in the time domain. A collection of 1387 images from 212 ex vivo tissue samples from 199 participants requiring a histopathologic examination of the cervix has been created. Images from this collection were assessed in respect to their benefit in providing markets or evidence of early developments representative of cervical cancer. In our images, the contrast in dense tissue is weak and specific markers that could be associated with a higher cancer risk were difficult to establish. For two reasons it was decided to use an algorithm for classifying the images: 1) Modern OCT systems acquire gigabytes of data per second which cannot be assessed in a clinically meaningful time. 2) An unsupervised classification tool can provide an objective assessment. There is no established method for evaluating OCT images of dense tissue. A classification algorithm was designed that uses Principal Components Analysis as means of data reduction and Linear Discriminant Analysis as a classification tool. This approach does not rely on clinical markets to be designated a priori. The algorithm was applied to the clinical data set to separate samples with mild from severe risk of cancer progression. The performance after leave-one-patient-out cross-validation reaches 61.5% (sensitivity = 66.7%, specificity = 47.3%, kappa = 0.52). These results are not convincing enough to let OCT replace current systems as clinical tools in cervical precancer assessment. Routes for improving results are suggested. This thesis provides a novel, generic algorithm for rapidly classifying OCT data obtained from dense tissues

Towards automated classification of clinical optimal coherence tomography (OCT) data obtained from dense tissues

Cervical cancer can be prevented if its precursors are recognised. Those lesions that justify preventive treatment are currently identified using methods that suffer from delayed results, false positives and subjective judgement. Optical coherence tomography (OCT) is a novel imaging modality that provides high-resolution backscattering data similar to ultrasonography. It could potentially provide in vivo and real-time imaging from within the entire cervical epithelium, where cervical cancer predominantly develops. In this study, we used a bench top OCT system with a 1310 nm light source. It employs fibre optics and operates in the time domain. A collection of 1387 images from 212 ex vivo tissue samples from 199 participants requiring a histopathologic examination of the cervix has been created. Images from this collection were assessed in respect to their benefit in providing markets or evidence of early developments representative of cervical cancer. In our images, the contrast in dense tissue is weak and specific markers that could be associated with a higher cancer risk were difficult to establish. For two reasons it was decided to use an algorithm for classifying the images: 1) Modern OCT systems acquire gigabytes of data per second which cannot be assessed in a clinically meaningful time. 2) An unsupervised classification tool can provide an objective assessment. There is no established method for evaluating OCT images of dense tissue. A classification algorithm was designed that uses Principal Components Analysis as means of data reduction and Linear Discriminant Analysis as a classification tool. This approach does not rely on clinical markets to be designated a priori. The algorithm was applied to the clinical data set to separate samples with mild from severe risk of cancer progression. The performance after leave-one-patient-out cross-validation reaches 61.5% (sensitivity = 66.7%, specificity = 47.3%, kappa = 0.52). These results are not convincing enough to let OCT replace current systems as clinical tools in cervical precancer assessment. Routes for improving results are suggested. This thesis provides a novel, generic algorithm for rapidly classifying OCT data obtained from dense tissues.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Computer Aided Interpretation Approach for Optical Tomographic Images

A computer-aided interpretation approach is proposed to detect rheumatic arthritis (RA) of human finger joints in optical tomographic images. The image interpretation method employs a multi-variate signal detection analysis aided by a machine learning classification algorithm, called Self-Organizing Mapping (SOM). Unlike in previous studies, this allows for combining multiple physical image parameters, such as minimum and maximum values of the absorption coefficient for identifying affected and not affected joints. Classification performances obtained by the proposed method were evaluated in terms of sensitivity, specificity, Youden index, and mutual information. Different methods (i.e., clinical diagnostics, ultrasound imaging, magnet resonance imaging and inspection of optical tomographic images), were used as "ground truth"-benchmarks to determine the performance of image interpretations. Using data from 100 finger joints, findings suggest that some parameter combinations lead to higher sensitivities while others to higher specificities when compared to single parameter classifications employed in previous studies. Maximum performances were reached when combining minimum/maximum-ratio and image variance with respect to ultra sound as benchmark. In this case, sensitivity and specificity of 0.94 and 0.96 respectively were achieved. These values are much higher than results reported when a) other classification techniques were applied or b) single parameter classifications were used, where sensitivities and specificities of 0.71 were achieved.Comment: This paper has been withdrawn by the authors. A newer version of this manuscript is in peer-review at the Journal of Biomedical Optic