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

Zero-shot super-resolution with a physically-motivated downsampling kernel for endomicroscopy

Super-resolution (SR) methods have seen significant advances thanks to the development of convolutional neural networks (CNNs). CNNs have been successfully employed to improve the quality of endomicroscopy imaging. Yet, the inherent limitation of research on SR in endomicroscopy remains the lack of ground truth high-resolution (HR) images, commonly used for both supervised training and reference-based image quality assessment (IQA). Therefore, alternative methods, such as unsupervised SR are being explored. To address the need for non-reference image quality improvement, we designed a novel zero-shot super-resolution (ZSSR) approach that relies only on the endomicroscopy data to be processed in a self-supervised manner without the need for ground-truth HR images. We tailored the proposed pipeline to the idiosyncrasies of endomicroscopy by introducing both: a physically-motivated Voronoi downscaling kernel accounting for the endomicroscope’s irregular fibre-based sampling pattern, and realistic noise patterns. We also took advantage of video sequences to exploit a sequence of images for self-supervised zero-shot image quality improvement. We run ablation studies to assess our contribution in regards to the downscaling kernel and noise simulation. We validate our methodology on both synthetic and original data. Synthetic experiments were assessed with reference-based IQA, while our results for original images were evaluated in a user study conducted with both expert and non-expert observers. The results demonstrated superior performance in image quality of ZSSR reconstructions in comparison to the baseline method. The ZSSR is also competitive when compared to supervised single-image SR, especially being the preferred reconstruction technique by experts

Learning from irregularly sampled data for endomicroscopy super-resolution: a comparative study of sparse and dense approaches

PURPOSE: Probe-based confocal laser endomicroscopy (pCLE) enables performing an optical biopsy via a probe. pCLE probes consist of multiple optical fibres arranged in a bundle, which taken together generate signals in an irregularly sampled pattern. Current pCLE reconstruction is based on interpolating irregular signals onto an over-sampled Cartesian grid, using a naive linear interpolation. It was shown that convolutional neural networks (CNNs) could improve pCLE image quality. Yet classical CNNs may be suboptimal in regard to irregular data. METHODS: We compare pCLE reconstruction and super-resolution (SR) methods taking irregularly sampled or reconstructed pCLE images as input. We also propose to embed a Nadaraya-Watson (NW) kernel regression into the CNN framework as a novel trainable CNN layer. We design deep learning architectures allowing for reconstructing high-quality pCLE images directly from the irregularly sampled input data. We created synthetic sparse pCLE images to evaluate our methodology. RESULTS: The results were validated through an image quality assessment based on a combination of the following metrics: peak signal-to-noise ratio and the structural similarity index. Our analysis indicates that both dense and sparse CNNs outperform the reconstruction method currently used in the clinic. CONCLUSION: The main contributions of our study are a comparison of sparse and dense approach in pCLE image reconstruction. We also implement trainable generalised NW kernel regression as a novel sparse approach. We also generated synthetic data for training pCLE SR

Convergence of the all-time supremum of a L\'evy process in the heavy-traffic regime

In this paper we derive a technique of obtaining limit theorems for suprema of L\'evy processes from their random walk counterparts. For each $a>0$, let $\{Y^{(a)}_n:n\ge 1\}$ be a sequence of independent and identically distributed random variables and $\{X^{(a)}_t:t\ge 0\}$ be a L\'evy processes such that $X_1^{(a)}\stackrel{d}{=} Y_1^{(a)}$, $\mathbb E X_1^{(a)}<0$ and $\mathbb E X_1^{(a)}\uparrow0$ as $a\downarrow0$. Let $S^{(a)}_n=\sum_{k=1}^n Y^{(a)}_k$. Then, under some mild assumptions, $\Delta(a)\max_{n\ge 0} S_n^{(a)}\stackrel{d}{\to} R\iff\Delta(a)\sup_{t\ge 0} X^{(a)}_t\stackrel{d}{\to} R$, for some random variable $R$ and some function $\Delta(\cdot)$. We utilize this result to present a number of limit theorems for suprema of L\'evy processes in the heavy-traffic regime

Adversarial training with cycle consistency for unsupervised super-resolution in endomicroscopy

In recent years, endomicroscopy has become increasingly used for diagnostic purposes and interventional guidance. It can provide intraoperative aids for real-time tissue characterization and can help to perform visual investigations aimed for example to discover epithelial cancers. Due to physical constraints on the acquisition process, endomicroscopy images, still today have a low number of informative pixels which hampers their quality. Post-processing techniques, such as Super-Resolution (SR), are a potential solution to increase the quality of these images. SR techniques are often supervised, requiring aligned pairs of low-resolution (LR) and high-resolution (HR) images patches to train a model. However, in our domain, the lack of HR images hinders the collection of such pairs and makes supervised training unsuitable. For this reason, we propose an unsupervised SR framework based on an adversarial deep neural network with a physically-inspired cycle consistency, designed to impose some acquisition properties on the super-resolved images. Our framework can exploit HR images, regardless of the domain where they are coming from, to transfer the quality of the HR images to the initial LR images. This property can be particularly useful in all situations where pairs of LR/HR are not available during the training. Our quantitative analysis, validated using a database of 238 endomicroscopy video sequences from 143 patients, shows the ability of the pipeline to produce convincing super-resolved images. A Mean Opinion Score (MOS) study also confirms this quantitative image quality assessment

Gaussian queues in light and heavy traffic

In this paper we investigate Gaussian queues in the light-traffic and in the heavy-traffic regime. The setting considered is that of a centered Gaussian process $X\equiv\{X(t):t\in\mathbb R\}$ with stationary increments and variance function $\sigma^2_X(\cdot)$, equipped with a deterministic drift $c>0$, reflected at 0: $$Q_X^{(c)}(t)=\sup_{-\infty<s\le t}(X(t)-X(s)-c(t-s)).$$ We study the resulting stationary workload process $Q^{(c)}_X\equiv\{Q_X^{(c)}(t):t\ge0\}$ in the limiting regimes $c\to 0$ (heavy traffic) and $c\to\infty$ (light traffic). The primary contribution is that we show for both limiting regimes that, under mild regularity conditions on the variance function, there exists a normalizing function $\delta(c)$ such that $Q^{(c)}_X(\delta(c)\cdot)/\sigma_X(\delta(c))$ converges to a non-trivial limit in $C[0,\infty)$

Clinical and Epidemiologic Research Case-Control Pilot Study of Soft Contact Lens Wearers With Corneal Infiltrative Events and Healthy Controls

PURPOSE. The purpose of this study was to assess risk factors associated with soft contact lens (SCL)-related corneal infiltrative events (CIEs). METHODS. This was a single-visit, case-control study conducted at five academic centers in North America. Cases were defined as current SCL wearers with a symptomatic CIE. For each case, three age-and sex-matched controls were enrolled. Subjects completed the Contact Lens Risk Survey (CLRS), a standardized scripted medical interview, supplied a recent health history, and underwent an ocular examination. Microbial culturing of the ocular surface, SCL, and lens storage case was conducted for all cases and one of the three matched controls. Univariate and multivariate logistic regression modeling were used to assess the risk of developing a CIE. RESULTS. Thirty cases and 90 controls 13 to 31 years of age completed the study. Corneal infiltrative event diagnosis included contact lens-associated red eye, infiltrative keratitis, and contact lens peripheral ulcer. Subjects with symptomatic CIEs were more likely to harbor substantial levels of gram-negative bioburden on the ocular surface and contact lens. Significant risk factors for developing a CIE were overnight wear of SCLs, use of multipurpose solution, rinsing SCLs with water, lens storage case older than 6 months, previous &apos;&apos;red eye&apos;&apos; event, use of ocular drops in the past week, and illness during the past week. CONCLUSIONS. This pilot study demonstrated feasibility of enrolling a representative pool of SCL wearers with an untreated, symptomatic CIE and assessing CIE risk factors by using standardized methods. A larger sample size is needed to determine relationships between patient-reported behaviors and exposures, microbial bioburden, and CIE development. Keywords: adverse events, contact lenses, corneal infiltrative events, microbial culturing A recent report from the US Centers for Disease Control and Prevention (CDC) called to light the substantial burden associated with contact lens-related complications. 1 The CDC report estimated that contact lens-related keratitis results in nearly 1 million doctor visits each year and carries an associated cost of $175 million. 1 This estimate does not include the additional &apos;&apos;costs&apos;&apos; to the patient such as pain or discomfort, missed school or work, and potential for permanent loss of vision. Approximately 37 million people in the United States currently wear contact lenses and, due to the increasing prevalence of myopia, more and younger patients are expected to begin wearing contact lenses to aid in its management

New Strategy for Rapid Diagnosis and Characterization of Fungal Infections: The Example of Corneal Scrapings

PURPOSE: The prognosis of people infected with Fungi especially immunocompromised depends on rapid and accurate diagnosis to capitalize on time administration of specific treatments. However, cultures produce false negative results and nucleic-acid amplification techniques require complex post-amplification procedures to differentiate relevant fungal types. The objective of this work was to develop a new diagnostic strategy based on real-time polymerase-chain reaction high-resolution melting analysis (PCR-HRM) that a) detects yeasts and filamentous Fungi, b) differentiates yeasts from filamentous Fungi, and c) discriminates among relevant species of yeasts. METHODS: PCR-HRM detection limits and specificity were assessed with a) isolated strains; b) human blood samples experimentally infected with Fungi; c) blood experimentally infected with other infectious agents; d) corneal scrapings from patients with suspected fungal keratitis (culture positive and negative) and e) scrapings from patients with suspected bacterial, viral or Acanthamoeba infections. The DNAs were extracted and mixed with primers diluted in the MeltDoctor® HRM Master Mix in 2 tubes, the first for yeasts, containing the forward primer CandUn (5'CATGCCTGTTTGAGCGTC) and the reverse primer FungUn (5'TCCTCCGCTT ATTGATATGCT) and the second for filamentous Fungi, containing the forward primer FilamUn (5'TGCCTGTCCGAGCGTCAT) and FungUn. Molecular probes were not necessary. The yields of DNA extraction and the PCR inhibitors were systematically monitored. RESULTS: PCR-HRM detected 0.1 Colony Forming Units (CFU)/µl of yeasts and filamentous Fungi, differentiated filamentous Fungi from yeasts and discriminated among relevant species of yeasts. PCR-HRM performances were higher than haemoculture and sensitivity and specificity was 100% for culture positive samples, detecting and characterizing Fungi in 7 out 10 culture negative suspected fungal keratitis. CONCLUSIONS: PCR-HRM appears as a new, sensitive, specific and inexpensive test that detects Fungi and differentiates filamentous Fungi from yeasts. It allows direct fungal detection from clinical samples and experimentally infected blood in less than 2.30 h after DNA extraction

Catalytic wet air oxidation of phenol in a trickle bed reactor operating at periodic liquid flow modulation

W publikacji przedstawiono wyniki badań doświadczalnych procesu ultrafiltracji modelowych i przemysłowych solanek z zastosowaniem membran ceramicznych 150 kDa. Głównym celem badań była analiza wpływu dwóch podstawowych parametrów operacyjnych, stężenia chlorku sodu w nadawie i ciśnienia transmembranowego TMP m fouling badanej membrany ceramicznej Al2O3/TiO2/ZrO2.Results of ultrafiltration studies using ceramic 150 kDa membrane and model solutions containing bovine serum albumin and sodium chloride as well as salted wastewater from fish industry are reported. A main objective of this work was to study the effect of two main operating parameters, i.e. sodium chloride concentration in feed solution and transmembrane pressure, TMP, on fouling behavior of ceramic Al2O3/TiO2ZrO2. membrane during ultrafiltration tests