Effective deep learning training for single-image super-resolution in endomicroscopy exploiting video-registration-based reconstruction

PURPOSE: Probe-based confocal laser endomicroscopy (pCLE) is a recent imaging modality that allows performing in vivo optical biopsies. The design of pCLE hardware, and its reliance on an optical fibre bundle, fundamentally limits the image quality with a few tens of thousands fibres, each acting as the equivalent of a single-pixel detector, assembled into a single fibre bundle. Video registration techniques can be used to estimate high-resolution (HR) images by exploiting the temporal information contained in a sequence of low-resolution (LR) images. However, the alignment of LR frames, required for the fusion, is computationally demanding and prone to artefacts. METHODS: In this work, we propose a novel synthetic data generation approach to train exemplar-based Deep Neural Networks (DNNs). HR pCLE images with enhanced quality are recovered by the models trained on pairs of estimated HR images (generated by the video registration algorithm) and realistic synthetic LR images. Performance of three different state-of-the-art DNNs techniques were analysed on a Smart Atlas database of 8806 images from 238 pCLE video sequences. The results were validated through an extensive image quality assessment that takes into account different quality scores, including a Mean Opinion Score (MOS). RESULTS: Results indicate that the proposed solution produces an effective improvement in the quality of the obtained reconstructed image. CONCLUSION: The proposed training strategy and associated DNNs allows us to perform convincing super-resolution of pCLE images

Novel OCT mosaicking pipeline with Feature- and Pixel-based registration

High-resolution Optical Coherence Tomography (OCT) images are crucial for ophthalmology studies but are limited by their relatively narrow field of view (FoV). Image mosaicking is a technique for aligning multiple overlapping images to obtain a larger FoV. Current mosaicking pipelines often struggle with substantial noise and considerable displacement between the input sub-fields. In this paper, we propose a versatile pipeline for stitching multi-view OCT/OCTA \textit{en face} projection images. Our method combines the strengths of learning-based feature matching and robust pixel-based registration to align multiple images effectively. Furthermore, we advance the application of a trained foundational model, Segment Anything Model (SAM), to validate mosaicking results in an unsupervised manner. The efficacy of our pipeline is validated using an in-house dataset and a large public dataset, where our method shows superior performance in terms of both accuracy and computational efficiency. We also made our evaluation tool for image mosaicking and the corresponding pipeline publicly available at \url{https://github.com/MedICL-VU/OCT-mosaicking}

Effective deep learning training for single-image super-resolution in endomicroscopy exploiting video-registration-based reconstruction

Purpose: Probe-based Confocal Laser Endomicroscopy (pCLE) is a recent imaging modality that allows performing in vivo optical biopsies. The design of pCLE hardware, and its reliance on an optical fibre bundle, fundamentally limits the image quality with a few tens of thousands fibres, each acting as the equivalent of a single-pixel detector, assembled into a single fibre bundle. Video-registration techniques can be used to estimate high-resolution (HR) images by exploiting the temporal information contained in a sequence of low-resolution (LR) images. However, the alignment of LR frames, required for the fusion, is computationally demanding and prone to artefacts. Methods: In this work, we propose a novel synthetic data generation approach to train exemplar-based Deep Neural Networks (DNNs). HR pCLE images with enhanced quality are recovered by the models trained on pairs of estimated HR images (generated by the video-registration algorithm) and realistic synthetic LR images. Performance of three different state-of-the-art DNNs techniques were analysed on a Smart Atlas database of 8806 images from 238 pCLE video sequences. The results were validated through an extensive Image Quality Assessment (IQA) that takes into account different quality scores, including a Mean Opinion Score (MOS). Results: Results indicate that the proposed solution produces an effective improvement in the quality of the obtained reconstructed image. Conclusion: The proposed training strategy and associated DNNs allows us to perform convincing super-resolution of pCLE images

UAV based distributed automatic target detection algorithm under realistic simulated environmental effects

Over the past several years, the military has grown increasingly reliant upon the use of unattended aerial vehicles (UAVs) for surveillance missions. There is an increasing trend towards fielding swarms of UAVs operating as large-scale sensor networks in the air [1]. Such systems tend to be used primarily for the purpose of acquiring sensory data with the goal of automatic detection, identification, and tracking objects of interest. These trends have been paralleled by advances in both distributed detection [2], image/signal processing and data fusion techniques [3]. Furthermore, swarmed UAV systems must operate under severe constraints on environmental conditions and sensor limitations. In this work, we investigate the effects of environmental conditions on target detection performance in a UAV network. We assume that each UAV is equipped with an optical camera, and use a realistic computer simulation to generate synthetic images. The automatic target detector is a cascade of classifiers based on Haar-like features. The detector\u27s performance is evaluated using simulated images that closely mimic data acquired in a UAV network under realistic camera and environmental conditions. In order to improve automatic target detection (ATD) performance in a swarmed UAV system, we propose and design several fusion techniques both at the image and score level and analyze both the case of a single observation and the case of multiple observations of the same target

3D confocal laser-scanning microscopy for large-area imaging of the corneal subbasal nerve plexus

The capability of corneal confocal microscopy (CCM) to acquire high-resolution in vivo images of the densely innervated human cornea has gained considerable interest in using this non-invasive technique as an objective diagnostic tool for staging peripheral neuropathies. Morphological alterations of the corneal subbasal nerve plexus (SNP) assessed by CCM have been shown to correlate well with the progression of neuropathic diseases and even predict future-incident neuropathy. Since the field of view of single CCM images is insufficient for reliable characterisation of nerve morphology, several image mosaicking techniques have been developed to facilitate the assessment of the SNP in large-area visualisations. Due to the limited depth of field of confocal microscopy, these approaches are highly sensitive to small deviations of the focus plane from the SNP layer. Our contribution proposes a new automated solution, combining guided eye movements for rapid expansion of the acquired SNP area and axial focus plane oscillations to guarantee complete imaging of the SNP. We present results of a feasibility study using the proposed setup to evaluate different oscillation settings. By comparing different image selection approaches, we show that automatic tissue classification algorithms are essential to create high-quality mosaic images from the acquired 3D dataset

High-resolution fluorescence endomicroscopy for rapid evaluation of breast cancer margins

Breast cancer is a major public health problem world-wide and the second leading cause of cancer-related female deaths. Breast conserving surgery (BCS), in the form of wide local excision (WLE), allows complete tumour resection while maintaining acceptable cosmesis. It is the recommended treatment for a large number of patients with early stage disease or, in more advanced cases, following neoadjuvant chemotherapy. About 30% of patients undergoing BCS require one or more re-operative interventions, mainly due to the presence of positive margins. The standard of care for surgical margin assessment is post-operative examination of histopathological tissue sections. However, this process is invasive, introduces sampling errors and does not provide real-time assessment of the tumour status of radial margins. The objective of this thesis is to improve intra-operative assessment of margin status by performing optical biopsy in breast tissue. This thesis presents several technical and clinical developments related to confocal fluorescence endomicroscopy systems for real-time characterisation of different breast morphologies. The imaging systems discussed employ flexible fibre-bundle based imaging probes coupled to high-speed line-scan confocal microscope set-up. A preliminary study on 43 unfixed breast specimens describes the development and testing of line-scan confocal laser endomicroscope (LS-CLE) to image and classify different breast pathologies. LS-CLE is also demonstrated to assess the intra-operative tumour status of whole WLE specimens and surgical excisions with high diagnostic accuracy. A third study demonstrates the development and testing of a bespoke LS-CLE system with methylene blue (MB), an US Food and Drug Administration (FDA) approved fluorescent agent, and integration with robotic scanner to enable large-area in vivo imaging of breast cancer. The work also addresses three technical issues which limit existing fibre-bundle based fluorescence endomicroscopy systems: i) Restriction to use single fluorescence agent due to low-speed, single excitation and single fluorescence spectral band imaging systems; ii) Limited Field of view (FOV) of fibre-bundle endomicroscopes due to small size of the fibre tip and iii) Limited spatial resolution of fibre-bundle endomicroscopes due to the spacing between the individual fibres leading to fibre-pixelation effects. Details of design and development of a high-speed dual-wavelength LS-CLE system suitable for high-resolution multiplexed imaging are presented. Dual-wavelength imaging is achieved by sequentially switching between 488 nm and 660 nm laser sources for alternate frames, avoiding spectral bleed-through, and providing an effective frame rate of 60 Hz. A combination of hand-held or robotic scanning with real-time video mosaicking, is demonstrated to enable large-area imaging while still maintaining microscopic resolution. Finally, a miniaturised piezoelectric transducer-based fibre-shifting endomicroscope is developed to enhance the resolution over conventional fibre-bundle based imaging systems. The fibre-shifting endomicroscope provides a two-fold improvement in resolution and coupled to a high-speed LS-CLE scanning system, provides real-time imaging of biological samples at 30 fps. These investigations furthered the utility and applications of the fibre-bundle based fluorescence systems for rapid imaging and diagnosis of cancer margins.Open Acces

Automatic motion compensation for structured illumination endomicroscopy using a flexible fiber bundle

Significance: Confocal laser scanning enables optical sectioning in clinical fiber bundle endomicroscopes, but lower-cost, simplified endomicroscopes use widefield incoherent illumination instead. Optical sectioning can be introduced in these simple systems using structured illumination microscopy (SIM), a multiframe digital subtraction process. However, SIM results in artifacts when the probe is in motion, making the technique difficult to use in vivo and preventing the use of mosaicking to synthesize a larger effective field of view (FOV). Aim: We report and validate an automatic motion compensation technique to overcome motion artifacts and allow generation of mosaics in SIM endomicroscopy. Approach: Motion compensation is achieved using image registration and real-time pattern orientation correction via a digital micromirror device. We quantify the similarity of moving probe reconstructions to those acquired with a stationary probe using the relative mean of the absolute differences (MAD). We further demonstrate mosaicking with a moving probe in mechanical and freehand operation. Results: Reconstructed SIM images show an improvement in the MAD from 0.85 to 0.13 for lens paper and from 0.27 to 0.12 for bovine tissue. Mosaics also show vastly reduced artifacts. Conclusion: The reduction in motion artifacts in individual SIM reconstructions leads to mosaics that more faithfully represent the morphology of tissue, giving clinicians a larger effective FOV than the probe itself can provide

A high sensitivity, low noise and high spatial resolution multi-band infrared reflectography camera for the study of paintings and works on paper

Infrared reflectography (IRR) remains an important method to visualize underdrawing and compositional changes in paintings. Older IRR camera systems are being replaced with near-infrared cameras consisting of room temperature infrared detector arrays made out of indium gallium arsenide (InGaAs) that operate over the spectral range of ~900 to 1700 nm. Two camera types are becoming prevalent. The first is staring array infrared cameras having 0.25–1 Megapixels where the camera or painting is moved to acquire tens of individual images that are later mosaicked together to create the infrared reflectogram. The second camera type is scanning back cameras in which a small InGaAs array (linear or area array) is mechanically scanned over a large image formed by the camera lens to create the reflectogram, typically 16 Megapixels. Both systems have advantages and disadvantages. The staring IR array cameras offer more flexible collection formats, provide live images, and allow for the use of spectral bandpass filters that can provide reflectograms with better contrast in some cases. They do require a mechanical system for moving the camera or the artwork and post-capture image mosaicking. Scanning back cameras eliminate or reduce the amount of mosaicking and movement of the camera, however the need to minimize light exposure to the artwork requires short integration times, and thus limits the use of spectral bandpass filters. In general, InGaAs cameras are not sensitive in the 1700 to ~2300 nm spectral region, which has been identified in prior studies as useful for examining paintings with copper green pigments or thick lead white paints. Prior studies using cameras with sensitivity from 1000 to 2500 nm have found in general the performance at wavelengths longer than 1700 nm degraded relative to the performance at shorter wavelengths. Thus, there is interest in a camera system having improved performance out to 2500 nm that can utilize spectral bandpass filters

Development Of A High Performance Mosaicing And Super-Resolution Algorithm

In this dissertation, a high-performance mosaicing and super-resolution algorithm is described. The scale invariant feature transform (SIFT)-based mosaicing algorithm builds an initial mosaic which is iteratively updated by the robust super resolution algorithm to achieve the final high-resolution mosaic. Two different types of datasets are used for testing: high altitude balloon data and unmanned aerial vehicle data. To evaluate our algorithm, five performance metrics are employed: mean square error, peak signal to noise ratio, singular value decomposition, slope of reciprocal singular value curve, and cumulative probability of blur detection. Extensive testing shows that the proposed algorithm is effective in improving the captured aerial data and the performance metrics are accurate in quantifying the evaluation of the algorithm