Improving utility of brain tumor confocal laser endomicroscopy: objective value assessment and diagnostic frame detection with convolutional neural networks

Confocal laser endomicroscopy (CLE), although capable of obtaining images at cellular resolution during surgery of brain tumors in real time, creates as many non-diagnostic as diagnostic images. Non-useful images are often distorted due to relative motion between probe and brain or blood artifacts. Many images, however, simply lack diagnostic features immediately informative to the physician. Examining all the hundreds or thousands of images from a single case to discriminate diagnostic images from nondiagnostic ones can be tedious. Providing a real-time diagnostic value assessment of images (fast enough to be used during the surgical acquisition process and accurate enough for the pathologist to rely on) to automatically detect diagnostic frames would streamline the analysis of images and filter useful images for the pathologist/surgeon. We sought to automatically classify images as diagnostic or non-diagnostic. AlexNet, a deep-learning architecture, was used in a 4-fold cross validation manner. Our dataset includes 16,795 images (8572 nondiagnostic and 8223 diagnostic) from 74 CLE-aided brain tumor surgery patients. The ground truth for all the images is provided by the pathologist. Average model accuracy on test data was 91% overall (90.79 % accuracy, 90.94 % sensitivity and 90.87 % specificity). To evaluate the model reliability we also performed receiver operating characteristic (ROC) analysis yielding 0.958 average for the area under ROC curve (AUC). These results demonstrate that a deeply trained AlexNet network can achieve a model that reliably and quickly recognizes diagnostic CLE images.Comment: SPIE Medical Imaging: Computer-Aided Diagnosis 201

Fast widefield techniques for fluorescence and phase endomicroscopy

Thesis (Ph.D.)--Boston UniversityEndomicroscopy is a recent development in biomedical optics which gives researchers and physicians microscope-resolution views of intact tissue to complement macroscopic visualization during endoscopy screening. This thesis presents HiLo endomicroscopy and oblique back-illumination endomicroscopy, fast widefield imaging techniques with fluorescence and phase contrast, respectively. Fluorescence imaging in thick tissue is often hampered by strong out-of-focus background signal. Laser scanning confocal endomicroscopy has been developed for optically-sectioned imaging free from background, but reliance on mechanical scanning fundamentally limits the frame rate and represents significant complexity and expense. HiLo is a fast, simple, widefield fluorescence imaging technique which rejects out-of-focus background signal without the need for scanning. It works by acquiring two images of the sample under uniform and structured illumination and synthesizing an optically sectioned result with real-time image processing. Oblique back-illumination microscopy (OBM) is a label-free technique which allows, for the first time, phase gradient imaging of sub-surface morphology in thick scattering tissue with a reflection geometry. OBM works by back-illuminating the sample with the oblique diffuse reflectance from light delivered via off-axis optical fibers. The use of two diametrically opposed illumination fibers allows simultaneous and independent measurement of phase gradients and absorption contrast. Video-rate single-exposure operation using wavelength multiplexing is demonstrated

Comparing probe-based confocal laser endomicroscopy with histology. Are we looking at the same picture?

In conclusion, we found for the first time a positive relationship between a long sleep duration and some markers of melanoma aggressiveness. Future studies are needed to investigate the main pathophysiological mechanisms that could explain this association and the prognostic relevance of this findin

A roadmap for the clinical implementation of optical-imaging biomarkers

Clinical workflows for the non-invasive detection and characterization of disease states could benefit from optical-imaging biomarkers. In this Perspective, we discuss opportunities and challenges towards the clinical implementation of optical-imaging biomarkers for the early detection of cancer by analysing two case studies: the assessment of skin lesions in primary care, and the surveillance of patients with Barrett’s oesophagus in specialist care. We stress the importance of technical and biological validations and clinical-utility assessments, and the need to address implementation bottlenecks. In addition, we define a translational roadmap for the widespread clinical implementation of optical imaging-technologies

Online Super-Resolution For Fibre-Bundle-Based Confocal Laser Endomicroscopy

Probe-based Confocal Laser Endomicroscopy (pCLE) produces microscopic images enabling real-time in vivo optical biopsy. However, the miniaturisation of the optical hardware, specifically the reliance on an optical fibre bundle as an imaging guide, fundamentally limits image quality by producing artefacts, noise, and relatively low contrast and resolution. The reconstruction approaches in clinical pCLE products do not fully alleviate these problems. Consequently, image quality remains a barrier that curbs the full potential of pCLE. Enhancing the image quality of pCLE in real-time remains a challenge. The research in this thesis is a response to this need. I have developed dedicated online super-resolution methods that account for the physics of the image acquisition process. These methods have the potential to replace existing reconstruction algorithms without interfering with the fibre design or the hardware of the device. In this thesis, novel processing pipelines are proposed for enhancing the image quality of pCLE. First, I explored a learning-based super-resolution method that relies on mapping from the low to the high-resolution space. Due to the lack of high-resolution pCLE, I proposed to simulate high-resolution data and use it as a ground truth model that is based on the pCLE acquisition physics. However, pCLE images are reconstructed from irregularly distributed fibre signals, and grid-based Convolutional Neural Networks are not designed to take irregular data as input. To alleviate this problem, I designed a new trainable layer that embeds Nadaraya- Watson regression. Finally, I proposed a novel blind super-resolution approach by deploying unsupervised zero-shot learning accompanied by a down-sampling kernel crafted for pCLE. I evaluated these new methods in two ways: a robust image quality assessment and a perceptual quality test assessed by clinical experts. The results demonstrate that the proposed super-resolution pipelines are superior to the current reconstruction algorithm in terms of image quality and clinician preference

Identification of liver metastases with probe-based confocal laser endomicroscopy at two excitation wavelengths.

BACKGROUND: Metastasis of colorectal cancer to the liver is the most common indication for hepatic resection in a western population. Incomplete excision of malignancy due to residual microscopic disease normally results in worse patient outcome. Therefore, a method aiding in the real time discrimination of normal and malignant tissue on a microscopic level would be of benefit. MATERIAL AND METHODS: The ability of fluorescent probe-based confocal laser endomicroscopy (pCLE) to identify normal and malignant liver tissue was evaluated in an orthotopic murine model of colorectal cancer liver metastasis (CRLM). To maximise information yield, two clinical fluorophores, fluorescein and indocyanine green (ICG) were injected and imaged in a dual wavelength approach (488 and 660 nm, respectively). Visual tissue characteristics on pCLE examination were compared with histological features. Fluorescence intensity in both tissues was statistically analysed to elucidate if this can be used to differentiate between normal and malignant tissue. RESULTS: Fluorescein (488 nm) enabled good visualisation of normal and CRLM tissue, whereas ICG (660 nm) visualisation was limited to normal liver tissue only. Fluorescence intensity in areas of CRLM was typically 53-100% lower than normal hepatic parenchyma. Using general linear mixed modelling and receiver operating characteristic analysis, high fluorescence intensity was found to be statistically more likely in normal hepatic tissue. CONCLUSION: Real time discrimination between normal liver parenchyma and metastatic tissue with pCLE examination of fluorescein and ICG is feasible. Employing two (rather than a single) fluorophores allows a combination of qualitative and quantitative characteristics to be used to distinguish between hepatic parenchyma and CRLM. Lasers Surg. Med. © 2016 Wiley Periodicals, Inc