Applications of advanced spectroscopic imaging to biological tissues

The objectives of this research were to develop experimental approaches that can be applied to classify different stages of malignancy in routine formalin-fixed and paraffin-embedded tissues and to optimise the imaging approaches using novel implementations. It is hoped that the approach developed in this research may be applied for early cancer diagnostics in clinical settings in the future in order to increase cancer survival rates. Infrared spectroscopic imaging has recently shown to have great potential as a powerful method for the spatial visualization of biological tissues. This spectroscopic technique does not require sample labelling because its chemical specificity allows the differentiation of biocomponents to be achieved based on their chemical structures. Experiments were performed on 3-µm thick prostate and colon tissues that were deposited on 2 mm-calcium fluoride (CaF2) which were subsequently deparaffinised. The samples were measured under IR microscopes, in both transmission and attenuated total reflection (ATR) mode. In transmission, thermo-spectroscopic imaging of the prostate samples was first carried out to investigate the potential of thermography to complement the information obtained from IR spectral. Spectroscopic imaging has made the acquisition of chemical map of a sample possible within a short time span since this approach facilitates the simultaneous acquisition of thousands of spatially resolved infrared spectra. Spectral differences in the lipid region (3000 -2800 cm-1) were identified between cancer and benign regions within prostate tissues. The governing spectral band for classification was anti-symmetric stretching of CH2 (2921 cm-1) from PCA analysis. Nonetheless, the difference in tissue emissivity at room temperature was minimal, thus the contrast in the thermal image is low for intra-tissue classification. Besides, the thermal camera could only capture IR light between 3333-2000 cm-1. To record spectral data between 3900 - 900 cm-1 (mid-IR), Fourier transform infrared (FTIR) spectroscopic imaging was used to classify the different stages of colon disease. An automated processing framework was developed, that could achieve an overall classification accuracy of 92.7%. The processing steps included unsupervised k-means clustering of lipid bands, followed by Random Forest (RF) classification using the ‘fingerprint’ region of the data. The implementation of a correcting lens and the effect of the RMieS-EMSC correction on the tissue spectra were also investigated, which showed that computational RMieS-EMSC correction was more effective at removing spectral artefacts than the correcting lens. Furthermore, the effect of the fluctuations of surrounding humidity where the experiments were carried out was studied by using various supersaturated salt solutions. Significant peak changes of the phosphate band were observed, most notably the peak shift of the anti-symmetric stretching of phosphate bands from 1230 cm-1 to 1238 cm-1 was observed. By regulating and controlling humidity at its lowest, the classification accuracy of the colon specimens was improved without having to resort to alteration on the RF machine learning algorithm. In the ATR mode, additional apertures were introduced to the FTIR microscope, as a novel means of depth profiling the prostate tissue samples by changing the angle of incidence of IR light beam. Despite the successful attempts in capturing the qualitative information on the change of tissue morphology with the depth of penetration (dp), the spectral data were not suitable for further processing with machine learning as dp changes with wavelengths. Apart from the apertures, a ‘large-area’ germanium (Ge) crystal was introduced to enable simultaneous mapping and imaging of the colon tissue samples. Many advantages of this new implementation were observed, which included improvement in signal-to-noise ratio, uniform distribution, and no impression left on the sample. The research done in this thesis set a groundwork for clinical diagnosis and the novel implementations were transferable to studies of other samples.Open Acces

New Techniques in Gastrointestinal Endoscopy

As result of progress, endoscopy has became more complex, using more sophisticated devices and has claimed a special form. In this moment, the gastroenterologist performing endoscopy has to be an expert in macroscopic view of the lesions in the gut, with good skills for using standard endoscopes, with good experience in ultrasound (for performing endoscopic ultrasound), with pathology experience for confocal examination. It is compulsory to get experience and to have patience and attention for the follow-up of thousands of images transmitted during capsule endoscopy or to have knowledge in physics necessary for autofluorescence imaging endoscopy. Therefore, the idea of an endoscopist has changed. Examinations mentioned need a special formation, a superior level of instruction, accessible to those who have already gained enough experience in basic diagnostic endoscopy. This is the reason for what these new issues of endoscopy are presented in this book of New techniques in Gastrointestinal Endoscopy

Near-Infrared Autofluorescence Imaging and Spectroscopy for Early Detection of Precancer and Cancer in the Colon

Ph.DDOCTOR OF PHILOSOPH

An Investigation of the Diagnostic Potential of Autofluorescence Lifetime Spectroscopy and Imaging for Label-Free Contrast of Disease

The work presented in this thesis aimed to study the application of fluorescence lifetime spectroscopy (FLS) and fluorescence lifetime imaging microscopy (FLIM) to investigate their potential for diagnostic contrast of diseased tissue with a particular emphasis on autofluorescence (AF) measurements of gastrointestinal (GI) disease. Initially, an ex vivo study utilising confocal FLIM was undertaken with 420 nm excitation to characterise the fluorescence lifetime (FL) images obtained from 71 GI samples from 35 patients. A significant decrease in FL was observed between normal colon and polyps (p = 0.024), and normal colon and inflammatory bowel disease (IBD) (p = 0.015). Confocal FLIM was also performed on 23 bladder samples. A longer, although not significant, FL for cancer was observed, in paired specimens (n = 5) instilled with a photosensitizer. The first in vivo study was a clinical investigation of skin cancer using a fibre-optic FL spectrofluorometer and involved the interrogation of 27 lesions from 25 patients. A significant decrease in the FL of basal cell carcinomas compared to healthy tissue was observed (p = 0.002) with 445 nm excitation. A novel clinically viable FLS fibre-optic probe was then applied ex vivo to measure 60 samples collected from 23 patients. In a paired analysis of neoplastic polyps and normal colon obtained from the same region of the colon in the same patient (n = 12), a significant decrease in FL was observed (p = 0.021) with 435 nm excitation. In contrast, with 375 nm excitation, the mean FL of IBD specimens (n = 4) was found to be longer than that of normal tissue, although not statistically significant. Finally, the FLS system was applied in vivo in 17 patients, with initial data indicating that 435 nm excitation results in AF lifetimes that are broadly consistent with ex vivo studies, although no diagnostically significant differences were observed in the signals obtained in vivo.Open Acces

Confocal Laser Endomicroscopy Image Analysis with Deep Convolutional Neural Networks

abstract: Rapid intraoperative diagnosis of brain tumors is of great importance for planning treatment and guiding the surgeon about the extent of resection. Currently, the standard for the preliminary intraoperative tissue analysis is frozen section biopsy that has major limitations such as tissue freezing and cutting artifacts, sampling errors, lack of immediate interaction between the pathologist and the surgeon, and time consuming. Handheld, portable confocal laser endomicroscopy (CLE) is being explored in neurosurgery for its ability to image histopathological features of tissue at cellular resolution in real time during brain tumor surgery. Over the course of examination of the surgical tumor resection, hundreds to thousands of images may be collected. The high number of images requires significant time and storage load for subsequent reviewing, which motivated several research groups to employ deep convolutional neural networks (DCNNs) to improve its utility during surgery. DCNNs have proven to be useful in natural and medical image analysis tasks such as classification, object detection, and image segmentation. This thesis proposes using DCNNs for analyzing CLE images of brain tumors. Particularly, it explores the practicality of DCNNs in three main tasks. First, off-the shelf DCNNs were used to classify images into diagnostic and non-diagnostic. Further experiments showed that both ensemble modeling and transfer learning improved the classifier’s accuracy in evaluating the diagnostic quality of new images at test stage. Second, a weakly-supervised learning pipeline was developed for localizing key features of diagnostic CLE images from gliomas. Third, image style transfer was used to improve the diagnostic quality of CLE images from glioma tumors by transforming the histology patterns in CLE images of fluorescein sodium-stained tissue into the ones in conventional hematoxylin and eosin-stained tissue slides. These studies suggest that DCNNs are opted for analysis of CLE images. They may assist surgeons in sorting out the non-diagnostic images, highlighting the key regions and enhancing their appearance through pattern transformation in real time. With recent advances in deep learning such as generative adversarial networks and semi-supervised learning, new research directions need to be followed to discover more promises of DCNNs in CLE image analysis.Dissertation/ThesisDoctoral Dissertation Neuroscience 201

Preprocessing algorithms for the digital histology of colorectal cancer

Pre-processing techniques were developed for cell identification algorithms. These algorithms which locate and classify cells in digital microscopy images are important in digital pathology. The pre-processing methods included image sampling and colour normalisation for standard Haemotoxilyn and Eosin (H&E) images and co-localisation algorithms for multiplexed images. Data studied in the thesis came from patients with colorectal cancer. Patient histology images came from `The Cancer Genome Atlas' (TCGA), a repository with contributions from many different institutional sites. The multiplexed images were created by TIS, the Toponome Imaging System. Experiments with image sampling were applied to TCGA diagnostic images. The effect of sample size and sampling policy were evaluated. TCGA images were also used in experiments with colour normalisation algorithms. For TIS multiplexed images, probabilistic graphical models were developed as well as clustering applications. NW-BHC, an extension to Bayesian Hierarchical Clustering, was developed and, for TIS antibodies, applied to TCGA expression data. Using image sampling with a sample size of 100 tiles gave accurate prediction results while being seven to nine times faster than processing the entire image. The two most accurate colour normalisation methods were that of Macenko and a `Nave' algorithm. Accuracy varied by TCGA site, indicating that researchers should use several independent data sets when evaluating colour normalisation algorithms. Probabilistic graphical models, applied to multiplexed images, calculated links between pairs of antibodies. The application of clustering to cell nuclei resulted in two main groups, one associated with epithelial cells and the second associated with the stromal environment. For TCGA expression data and for several clustering metrics, NW-BHC improved on the standard EM algorithm

Multimodal optical systems for clinical oncology

This thesis presents three multimodal optical (light-based) systems designed to improve the capabilities of existing optical modalities for cancer diagnostics and theranostics. Optical diagnostic and therapeutic modalities have seen tremendous success in improving the detection, monitoring, and treatment of cancer. For example, optical spectroscopies can accurately distinguish between healthy and diseased tissues, fluorescence imaging can light up tumours for surgical guidance, and laser systems can treat many epithelial cancers. However, despite these advances, prognoses for many cancers remain poor, positive margin rates following resection remain high, and visual inspection and palpation remain crucial for tumour detection. The synergistic combination of multiple optical modalities, as presented here, offers a promising solution. The first multimodal optical system (Chapter 3) combines Raman spectroscopic diagnostics with photodynamic therapy using a custom-built multimodal optical probe. Crucially, this system demonstrates the feasibility of nanoparticle-free theranostics, which could simplify the clinical translation of cancer theranostic systems without sacrificing diagnostic or therapeutic benefit. The second system (Chapter 4) applies computer vision to Raman spectroscopic diagnostics to achieve spatial spectroscopic diagnostics. It provides an augmented reality display of the surgical field-of-view, overlaying spatially co-registered spectroscopic diagnoses onto imaging data. This enables the translation of Raman spectroscopy from a 1D technique to a 2D diagnostic modality and overcomes the trade-off between diagnostic accuracy and field-of-view that has limited optical systems to date. The final system (Chapter 5) integrates fluorescence imaging and Raman spectroscopy for fluorescence-guided spatial spectroscopic diagnostics. This facilitates macroscopic tumour identification to guide accurate spectroscopic margin delineation, enabling the spectroscopic examination of suspicious lesions across large tissue areas. Together, these multimodal optical systems demonstrate that the integration of multiple optical modalities has potential to improve patient outcomes through enhanced tumour detection and precision-targeted therapies.Open Acces