Endoscopic Fluorescence Imaging:Spectral Optimization and in vivo Characterization of Positive Sites by Magnifying Vascular Imaging

Since several decades, the physicians are able to access hollow organs with endoscopic methods, which serve both as diagnostic and surgical means in a wide range of disciplines of the modern medicine (e.g. urology, pneumology, gastroenterology). Unfortunately, white light (WL) endoscopy displays a limited sensitivity to early pre-cancerous lesions. Hence, several endoscopic methods based on fluorescence imaging have been developed to overcome this limitation. Both endogenous and exogenously-induced fluorescence have been investigated, leading to commercial products. Indeed, autofluorescence bronchoscopy, as well as porphyrin-based fluorescence cystoscopy, are now on the market. As a matter of fact, fluorescence-based endoscopic detection methods show very high sensitivity to pre-cancerous lesions, which are often overlooked in WL endoscopy, but they still lack specificity mainly due to the high false-positive rate. Although most of these false positives can easily be rejected under WL observation, tissue abnormalities such as inflammations, hyperplasia, and metaplasia are more difficult to identify, often resulting in supplementary biopsies. Therefore, the purpose of this thesis is to study novel, fast, and convenient method to characterize fluorescence positive spots in situ during fluorescence endoscopy and, more generally, to optimize the existing endoscopic setup. In this thesis, several clinical evaluations were conducted either in the tracheo-bronchial tree and the urinary bladder. In the urinary bladder, fluorescence imaging for detection of non-muscle invasive bladder cancer is based on the selective production and accumulation of fluorescing porphyrins, mainly protoporphyrin IX (PpIX), in cancerous tissues after the instillation of Hexvix® during one hour. In this thesis, we adapted a rigid cystoscope to perform high magnification (HM) cystoscopy in order to discriminate false from true fluorescence positive findings. Both white light and fluorescence modes are possible with the magnification cystoscope, allowing observation of the bladder wall with magnification ranging between 30× – for standard observation – and 650×. The optical zooming setup allows adjusting the magnification continuously in situ. In the high magnification regime, the smallest diameter of the field of view is 600 microns and the resolution is 2.5 microns, when in contact with the bladder wall. With this HM cystoscope, we characterized the superficial vascularization of the fluorescing sites in WL (370–700 nm) reflectance imaging in order to discriminate cancerous from non-cancerous tissues. This procedure allowed us to establish a classification based on observed vascular patterns. 72 patients subject to Hexvix® f luorescence cystoscopy were included in the study. Comparison of HM cystoscopy classification with histopathology results confirmed 32/33 (97%) cancerous biopsies, and rejected 17/20 (85%) non-cancerous lesions. No vascular alteration could be observed on the only positive lesion that was negative in HM mode, probably because this sarcomatoid carcinoma was not originating in the bladder mucosa. We established with this study that a magnification ranging between 80× and 100× is an optimal tradeoff to perform both macroscopic PDD and HM reflectance imaging. In order to make this approach more quantitative, different algorithms of image processing (vessel segmentation and skeletonisation, global information extraction) were also implemented in this thesis. In order to better visualize the vessels, we improved their contrast with respect to the background. Since hemoglobin is a very strong absorber, we targeted the two hemoglobin absorption peaks by placing appropriate bandpass filters (blue 405±50 nm, green 550±50 nm) in the light source. HM cystoscopy was then performed sequentially with WL, blue and green illumination. The two latter showed higher vessel-to-background contrast, identifying different layers of vascularization due to the light penetration depth. During fluorescence cystoscopy, we often observed that the images are somehow "blurred" by a greenish screen between endoscope tip and bladder mucosa. Since this effect is enhanced by the urine production, it is more visible with flexible scopes (lower flushing capabilities) and imaging systems that collect only autofluorescence as background. Indeed, when the bladder is not flushed regularly, greenish flows coming out of the ureters can easily be observed. For this reason, it is supposed that some fluorophores contained in the urine are excited by the photodetection excitation light, and appear greenish on the screen. This effect may impair the visualization of the bladder mucosa, and thus cancerous lesions, and lowers sensitivity of the fluorescence cystoscopy. In this thesis, we identified the main metabolites responsible for the liquid fluorescence, and optimized the spectral design accordingly. In the tracheo-bronchial tree, the fluorescence contrast is based on the sharp autofluorescence (AF) decrease on early cancerous lesions in the green spectral region (around 500 nm) and a relatively less important decrease in the red spectral region (> 600 nm) when excited with blue-violet light (around 410 nm). It has been shown over the last years, that this contrast may be attributed to a combined effect of epithelium thickening and higher concentration of hemoglobin in the tissues underneath the (pre-)cancerous lesions. In this thesis, we contributed to the definition of the input design of several new prototypes, that were subsequently tested in the clinical environment. We first showed that narrow-band excitation in the blue-violet could increase the tumor-to-normal spectral contrast in the green spectral region. Then, we quantified the intra- and inter-patient variations in the AF intensities in order to optimize the spectral response of the endoscopic fluorescence imaging system. For this purpose, we developed an endoscopic reference to be placed close to the bronchial mucosa during bronchoscopy. Finally, we evaluated a novel AF bronchoscope with blue-backscattered light on 144 patients. This new device showed increased sensitivity for pre-neoplastic lesions. Similar to what we observed in the bladder, it is likely that developing new imaging capabilities (including vascular imaging) will facilitate discriminating true from false positive in AF bronchoscopy. Here, we demonstrated that this magnification allowed us to resolve vessels with a diameter of about 30 µm. This resolution is likely to be sufficient to identify Shibuya's vascular criteria (loops, meshes, dotted vessels) on AF positive lesions. This criteria allow him to recognize pre-cancerous lesions, and thus can potentially decrease the false-positive rate with our AF imaging system. This magnification was also showed to be better for routine bronchoscopy, since it delivers sharper and more structured images to the operator

Detection of Intestinal Bleeding in Wireless Capsule Endoscopy using Machine Learning Techniques

Gastrointestinal (GI) bleeding is very common in humans, which may lead to fatal consequences. GI bleeding can usually be identified using a flexible wired endoscope. In 2001, a newer diagnostic tool, wireless capsule endoscopy (WCE) was introduced. It is a swallow-able capsule-shaped device with a camera that captures thousands of color images and wirelessly sends those back to a data recorder. After that, the physicians analyze those images in order to identify any GI abnormalities. But it takes a longer screening time which may increase the danger of the patients in emergency cases. It is therefore necessary to use a real-time detection tool to identify bleeding in the GI tract. Each material has its own spectral ‘signature’ which shows distinct characteristics in specific wavelength of light [33]. Therefore, by evaluating the optical characteristics, the presence of blood can be detected. In the study, three main hardware designs were presented: one using a two-wavelength based optical sensor and others using two six-wavelength based spectral sensors with AS7262 and AS7263 chips respectively to determine the optical characteristics of the blood and non-blood samples. The goal of the research is to develop a machine learning model to differentiate blood samples (BS) and non-blood samples (NBS) by exploring their optical properties. In this experiment, 10 levels of crystallized bovine hemoglobin solutions were used as BS and 5 food colors (red, yellow, orange, tan and pink) with different concentrations totaling 25 non-blood samples were used as NBS. These blood and non-blood samples were also combined with pig’s intestine to mimic in-vivo experimental environment. The collected samples were completely separated into training and testing data. Different spectral features are analyzed to obtain the optical information about the samples. Based on the performance on the selected most significant features of the spectral wavelengths, k-nearest neighbors algorithm (k-NN) is finally chosen for the automated bleeding detection. The proposed k-NN classifier model has been able to distinguish the BS and NBS with an accuracy of 91.54% using two wavelengths features and around 89% using three combined wavelengths features in the visible and near-infrared spectral regions. The research also indicates that it is possible to deploy tiny optical detectors to detect GI bleeding in a WCE system which could eliminate the need of time-consuming image post-processing steps

Tumour grading and discrimination based on class assignment and quantitative texture analysis techniques

Medical imaging represents the utilisation of technology in biology for the purpose of noninvasively revealing the internal structure of the organs of the human body. It is a way to improve the quality of the patient's life through a more precise and rapid diagnosis, and with limited side-effects, leading to an effective overall treatment procedure. The main objective of this thesis is to propose novel tumour discrimination techniques that cover both micro and macro-scale textures encountered in computed tomography (CI') and digital microscopy (DM) modalities, respectively. Image texture can provide significant information on the (ab)normality of tissue, and this thesis expands this idea to tumour texture grading and classification. The fractal dimension (FO) as a texture measure was applied to contrast enhanced CT lung tumour images in an aim to improve tumour grading accuracy from conventional CI' modality, and quantitative performance analysis showed an accuracy of 83.30% in distinguishing between advanced (aggressive) and early stage (non-aggressive) malignant tumours. A different approach was adopted for subtype discrimination of brain tumour OM images via a set of statistical and model-based texture analysis algorithms. The combined Gaussian Markov random field and run-length matrix texture measures outperformed all other combinations, achieving an overall class assignment classification accuracy of 92.50%. Also two new histopathological multi resolution approaches based on applying the FO as the best bases selection for discrete wavelet packet transform, and when fused with the Gabor filters' energy output improved the accuracy to 91.25% and 95.00%, respectively. While noise is quite common in all medical imaging modalities, the impact of noise on the applied texture measures was assessed as well. The developed lung and brain texture analysis techniques can improve the physician's ability to detect and analyse pathologies leading for a more reliable diagnosis and treatment of disease

Applications of Artificial Intelligence in Medicine Practice

This book focuses on a variety of interdisciplinary perspectives concerning the theory and application of artificial intelligence (AI) in medicine, medically oriented human biology, and healthcare. The list of topics includes the application of AI in biomedicine and clinical medicine, machine learning-based decision support, robotic surgery, data analytics and mining, laboratory information systems, and usage of AI in medical education. Special attention is given to the practical aspect of a study. Hence, the inclusion of a clinical assessment of the usefulness and potential impact of the submitted work is strongly highlighted

Characterising pattern asymmetry in pigmented skin lesions

Abstract. In clinical diagnosis of pigmented skin lesions asymmetric pigmentation is often indicative of melanoma. This paper describes a method and measures for characterizing lesion symmetry. The estimate of mirror symmetry is computed first for a number of axes at different degrees of rotation with respect to the lesion centre. The statistics of these estimates are the used to assess the overall symmetry. The method is applied to three different lesion representations showing the overall pigmentation, the pigmentation pattern, and the pattern of dermal melanin. The best measure is a 100% sensitive and 96% specific indicator of melanoma on a test set of 33 lesions, with a separate training set consisting of 66 lesions