Enhanced Vascular Features in Porcine Gastrointestinal Endoscopy Using Multispectral Imaging

Endoscopic investigation is a predominant stan-dard while assessing the gastrointestinal tract. Even though it has been rigorously used in diagnostics for many decades, a high miss rate has been recorded. Advanced endoscopic imaging still has not found solutions to problems like early cancer detection, polyp generality, disease classification, etc. One of the less explored techniques to study early cancer detection is spectral imaging which deals with the absorption and reflection spectra of various wavelengths of light by different layers of tissue. To study tissues under various illumination, a multi-spectral light source unit that can be used along with an endoscopy system was developed with 10 different LEDs of very narrow bandwidths. Using this light source, a feasibility study was per-formed on an animal in which the upper GI tract of a porcine model was imaged and sample images were taken for processing from five different sections. Some wavelengths showed better contrast enhancements for visualization of vascular structures. Wavelength 420 nm (violet light) showed better contrast and the gradient of the line profile histogram showed the highest intensity change between the blood vessels and the surrounding mucosa. These enhancements showed that spectral imaging can potentially help in studying tissues for early cancer detection and improved visualization of the G I tract using endoscopy.

Comparative Analysis of Resting Heart Rate Measurement at Multiple Instances in a Single Day

Resting Heart Rate (RHR) is used as an indicator of cardiovascular health and overall fitness. Clinically, RHR is measured from beat-to-beat heart rate data during the day when the body is at rest (RHR_rest), typically for ≥ 5 minutes. In this paper, we have compared the RHR measurements done at multiple instances in a single day namely, [Formula: see text], RHR immediately after waking up (RHR_morning) and RHR during sleep (RHR_sleep). The significance of measuring RHR_sleep and why it can be used as a potential replacement for the conventional methods is analysed through an experimental study in this paper. The results obtained using the proposed method stands out in terms of repeatability. RHR measurements were taken for 3 instances on a single day for 9 subjects on 5 alternate workdays. A comparative analysis was performed by measuring the repeatability coefficient (RC) and Standard Deviation (SD) on the RHR measurements taken during multiple instances for each subject separately. The average RC and SD over the 5 alternate workdays was 5 bpm and SD was 2 bpm for RHR_slep. For RHR_rest and RHR_morning, the average RC was 12 bpm and 11 bpm and the average SD was 5 bpm and 4 bpm respectively, which is comparatively higher. Hence this method can be potentially adopted instead of the conventional methods as the RHRsleep parameter is more reliable and precise due to its repeatable nature.

Deep learning based non-contact physiological monitoring in Neonatal Intensive Care Unit

Preterm babies in the Neonatal Intensive Care Unit (NICU) have to undergo continuous monitoring of their cardiac health. Conventional monitoring approaches are contact-based, making the neonates prone to various nosocomial infections. Video-based monitoring approaches have opened up potential avenues for contactless measurement. This work presents a pipeline for remote estimation of cardiopulmonary signals from videos in NICU setup. We have proposed an end-to-end deep learning (DL) model that integrates a non-learning-based approach to generate surrogate ground truth (SGT) labels for supervision, thus refraining from direct dependency on true ground truth labels. We have performed an extended qualitative and quantitative analysis to examine the efficacy of our proposed DL-based pipeline and achieved an overall average mean absolute error of 4.6 beats per minute (bpm) and root mean square error of 6.2 bpm in the estimated heart rate.

High Frame-Rate A-Mode Ultrasound System for Jugular Venous Pulse Tracking: A Feasibility Study

Jugular venous pulse (JVP) helps in the early detection of central venous pressure abnormalities and various cardiovascular diseases. Studies have been reported indicating that contour features of the JVP waveform provide crucial information regarding cardiac function. Although current ultrasound systems reliably provide the diameter measurements, they are limited by low frame rates resulting in poor resolution JVP cycles that are inadequate to yield distinguishable critical points. In this work, we propose an image-free high frame rate system for the assessment of JVP signals. The proposed A-mode ultrasound system acquires high fidelity JVP pulses with a temporal resolution of 4 ms and amplitude resolution of 10 µm. The functionality verification of the proposed system was performed by comparing it against a clinical-grade B-mode imaging system. A study was conducted on a cohort of 25 subjects in the 20-30 age group. While the system provided diameter measurements comparable to that of the imaging ones (r > 0.98, p < 0.05), it also yielded high-resolution JVP exhibiting the presence of all fiduciary points. This was a leveraging feature as opposed to the imaging system that possessed limited temporal and amplitude resolution. Clinical Relevance- The proposed system is a potential ultrasound means for measuring the diameter values from JV at the same time yielding the JVP critical points necessary for clinical analysis.

Improving Fast MRI Reconstructions with Pretext Learning in Low-Data Regime

Supervised deep learning methods have shown great promise for making magnetic resonance (MR) imaging scans faster. However, these supervised deep learning models need large volumes of labelled data to learn valuable representations and produce high-fidelity MR image reconstructions. The data used to train these models are often fully-sampled raw MR data, retrospectively under-sampled to simulate different MR acquisition acceleration factors. Obtaining high-quality, fully sampled raw MR data is costly and time-consuming. In this paper, we exploit the self supervision based learning by introducing a pretext method to boost feature learning using the more commonly available under-sampled MR data. Our experiments using different deep-learning-based reconstruction models in a low data regime demonstrate that self-supervision ensures stable training and improves MR image reconstruction.

Camera fusion for real-time temperature monitoring of neonates using deep learning

Abstract

The continuous monitoring of vital signs is a crucial aspect of medical care in neonatal intensive care units. Since cable-based sensors pose a potential risk for the immature skin of preterm infants, unobtrusive monitoring techniques using camera systems are increasingly investigated. The combination of deep learning–based algorithms and camera modalities such as RGB and infrared thermography can improve the development of cable-free methods for the extraction of vital parameters. In this study, a real-time approach for local extraction of temperatures on the body surface of neonates using a multi-modal clinical dataset was implemented. Therefore, a trained deep learning–based keypoint detector was used for body landmark prediction in RGB. Image registration was conducted to transfer the RGB points to the corresponding thermographic recordings. These landmarks were used to extract the body surface temperature in various regions to determine the central-peripheral temperature difference. A validation of the keypoint detector showed a mean average precision of 0.82. The registration resulted in mean absolute errors of 16.4 px (8.2 mm) for x and 22.4 px (11.2 mm) for y. The evaluation of the temperature extraction revealed a mean absolute error of 0.55 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{\circ }$$\end{document}∘C. A final performance of 31 fps was observed on the NVIDIA Jetson Xavier NX module, which proves real-time capability on an embedded GPU system. As a result, the approach can perform real-time temperature extraction on a low-cost GPU module.

Graphical abstract

Arterial pressure pulse wave separation analysis using a multi-gaussian decomposition model

OBJECTIVE

Methods for separating the forward-backward components from blood pulse waves rely on simultaneously measured pressure and flow velocity from a target artery site. Modelling approaches for flow velocity simplify the wave separation analysis (WSA), providing a methodological and instrumentational advantage over the former; however, current methods are limited to the aortic site. In this work, a multi-Gaussian decomposition (MGD) modelled WSA (MGDWSA) is developed for a non-aortic site asuch as the carotid artery. While the model is an adaptation of the existing wave separation theory, it does not rely on the information of measured or modelled flow velocity.

APPROACH

The proposed model decomposes the arterial pressure waveform using weighted and shifted multi-Gaussians, which are then uniquely combined to yield the forward (PF(t)) and backward (PB(t)) pressure wave. A study using the database of healthy (virtual) subjects was used to evaluate the performance of MGDWSA at the carotid artery and was compared against reference flow-based WSA methods.

MAIN RESULTS

The MGD modelled pressure waveform yielded a root-mean-square error (RMSE) < 0.35 mmHg. Reliable forward-backward components with a group average RMSE < 2.5 mmHg for PF(t) and PB(t) were obtained. When compared with the reference counterparts, the pulse pressures (ΔPF and ΔPB), as well as reflection quantification indices, showed a statistically significant strong correlation (r > 0.96, p < 0.0001) and (r > 0.83, p < 0.0001) respectively, with an insignificant (p > 0.05) bias.

SIGNIFICANCE

This study reports WSA for carotid pressure waveforms without assumptions on flow conditions. The proposed method has the potential to adapt and widen the vascular health assessment techniques incorporating pulse wave dynamics.

Cross fertilisation of Public Health and Translational Research

Public health is defined as the science of protecting the safety and improving the health of communities through education, policy-making and research for the prevention of disease (Gatseva and Argirova in J Public Health 19(3):205–6, 2011, 10.1007/s10389-011-0412-8; Winslow in Mod Med 2(1306):183–91, 1920. 10.1126/science.51.1306.23; What is public health. Centers for Disease Control Foundation. Centers for Disease Control, Atlanta, https://www.cdcfoundation.org/what-public-health; What is the WHO definition of health? from the Preamble to the Constitution of WHO as adopted by the International Health Conference, New York, On 7 April 1948. The definition has not been amended since. 22 July 1946; signed by the representatives of 61 States (Official Records of WHO, no. 2, p. 100) and entered into force, 19 June;1948. https://web.archive.org/web/20190307113324/https:/www.who.int/about/who-we-are/frequently-asked-questions). Translational research in healthcare is not only useful and satisfying for the researchers to bring their work to market but it would also support public health by bringing affordable, attainable and scalable solutions to the community at large. This is of high significance because instead of increasing the GDP spent in public health, we should focus on the increasing the translational research spending, as this would lead to improved solutions. Hence, the public health offering would reach a larger community at an improved cost. The COVID-19 pandemic and the huge number of lives it claimed exposes challenges in the public health. The pandemic has caused economic and social disruption to millions of people around the world, with many falling into extreme poverty. In early 2021, it was estimated nearly 690 million people are undernourished and by end of 2021 to increase further by 132 million (Joint statement by ILO, FAO, IFAD and WHO. Impact of COVID-19 on people's livelihoods, their health and our food systems https://www.who.int/news/item/13-10-2020-impact-of-covid-19-on-people's-livelihoods-their-health-and-our-food-systems). The spending for public health has increased many folds during the pandemic and this is where translational research in healthcare can play a transformative role to reduce the burden on government healthcare budget (Covid-19 and its impact on Indian society. https://timesofindia.indiatimes.com/readersblog/covid-19-and-its-impact-on-india/covid-19-and-its-impact-on-indian-society-27565/). Over the past decade, public health research has started playing a major role in Indian academic settings. COVID-19 pandemic has further highlighted the role of public health. However, the potential of using technological advancement has not been fully utilised. This is where translational research and public health can play a role to tap the full potential of technology. This review paper explores the public health practices to understand the different practices to examine how both public health and translational research can cross-fertilise. It concludes with a short discussion on implications on policymakers.

High-frame-rate A-mode ultrasound for calibration-free cuffless carotid pressure: feasibility study using lower body negative pressure intervention

PURPOSE

Existing technologies to measure central blood pressure (CBP) intrinsically depend on peripheral pressure or calibration models derived from it. Pharmacological or physiological interventions yielding different central and peripheral responses compromise the accuracy of such methods. We present a high-frame-rate ultrasound technology for cuffless and calibration-free evaluation of BP from the carotid artery. The system uses a pair of single-element ultrasound transducers to capture the arterial diameter and local pulse wave velocity (PWV) for the evaluation of beat-by-beat BP employing a novel biomechanical model.

MATERIALS AND METHODS

System's functionality assessment was conducted on eight male subjects (26 ± 4 years, normotensive and no history of cardiovascular risks) by perturbing pressure via short-term moderate lower body negative pressure (LBNP) intervention (-40 mmHg for 1 min). The ability of the system to capture dynamic responses of carotid pressure to LBNP was investigated and compared against the responses of peripheral pressure measured using a continuous BP monitor.

RESULTS

While the carotid pressure manifested trends similar to finger measurements during LBNP, the system also captured the differential carotid-to-peripheral pressure response, which corroborates the literature. The carotid diastolic and mean pressures agreed with the finger pressures (limits-of-agreement within ±7 mmHg) and exhibited acceptable uncertainty (mean absolute errors were 2.4 ± 3.5 and 2.6 ± 4.0 mmHg, respectively). Concurrent to the literature, the carotid systolic and pulse pressures (PPs) were significantly lower than those of the finger pressures by 11.1 ± 9.4 and 11.3 ± 8.2 mmHg, respectively (p < .0001).

CONCLUSIONS

The study demonstrated the method's potential for providing cuffless and calibration-free pressure measurements while reliably capturing the physiological aspects, such as PP amplification and dynamic pressure responses to intervention.

A Machine Learning Pipeline for Measurement of Arterial Stiffness in A-Mode Ultrasound

Arterial stiffness (AS) of the carotid artery is an early marker of stratifying cardiovascular disease risk. This article aims to improve the performance of ARTSENS, a noninvasive A-mode ultrasound-based device for measuring AS. The primary objective of ARTSENS is to enable the measurement of elastic modulus using A-Mode ultrasound and blood pressure. As this device is image-free, there is a need to automate: 1) carotid detection; 2) wall localization; and 3) inner lumen diameter measurement. This has been performed using conventional signal processing methods in some of the earlier works in this domain. In this article, deep neural network (DNN) models are employed to perform the above three tasks. The DNNs were trained over data acquired from 82 subjects at two different medical centers. Ground-truth labeling was performed by a trained operator using corresponding measurements from the state-of-the-art Aloka e-Tracking system. All three DNN models had significantly lower errors compared to earlier signal processing methods and could perform their measurements using a single A-Mode frame. Using the DNNs, two different machine learning pipelines have been proposed here to measure the elastic modulus; the best among them could achieve an error of 9.3% with the Pearson correlation coefficient of 0.94 ( ). The models were tested on Raspberry Pi and Jetson Nano single board computers to demonstrate real-time processing on low computational resources.

Community seroprevalence and risk factors for SARS CoV-2 infection in different subpopulations in Vellore, India and its implications for future prevention

Objectives

The aim of this study was to inform public health policy decisions through the assessment of IgG antibody seroprevalence in the population and the risk factors for SARS-CoV-2 infection.

Methods

The seroprevalence of IgG antibodies among different subpopulations at the end of the first and second waves of the pandemic was estimated. Various risk factors associated with seropositivity, including sociodemography, IgG antibodies against endemic human coronavirus, and vaccination status, were also assessed.

Results

For all 2433 consenting participants, the overall estimated seroprevalences at the end of first and second waves were 28.5% (95% CI 22.3–33.7%) and 71.5% (95% CI 62.8–80.5%), respectively. The accrual of IgG positivity was heterogeneous, with the highest seroprevalences found in urban slum populations (75.1%). Vaccine uptake varied among the subpopulations, with low rates (< 10%) among rural and urban slum residents. The majority of seropositive individuals (75%) were asymptomatic. Residence in urban slums (OR 2.02, 95% CI 1.57–2.6; p < 0.001), middle socioeconomic status (OR 1.77, 95% CI 1.17–2.67; p = 0.007), presence of diabetes (OR 1.721, 95% CI 1.148–2.581; p = 0.009), and hypertension (OR 1.75, 95% CI 1.16–2.64; p = 0.008) were associated with seropositivity in multivariable analyses.

Conclusion

Although considerable population immunity has been reached, with more than two-thirds seropositive, improved vaccination strategies among unreached subpopulations and high-risk individuals are suggested for better preparedness in future.

Separation of Forward-Backward Waves in the Arterial System using Multi-Gaussian Approach from Single Pulse Waveform

The arterial pulse waveform has an immense wealth of information in its morphology yet to be explored and translated to clinical practice. Wave separation analysis involves decomposing a pulse wave (pressure or diameter waveform) into a forward wave and a backward wave. The backward wave accumulates reflections due to arterial stiffness gradient, branching and geometric tapering of blood vessels across the arterial tree. The state-of-the-art wave separation analysis is based on estimating the input impedance of the target artery in the frequency/time domain, which requires simultaneously measured or modelled flow velocity and pressure waveform. We are proposing a new method of wave separation analysis using a multi-gaussian decomposition. The novelty of this approach is that it requires only a single pulse waveform at the target artery. Our method was compared against the triangular waveform-based impedance method. We successfully separated forward and backward waveform from the pressure waveform with maximum RMSE less than 5 mmHg and mean RMSE of 1.31 mmHg when compared against the triangular flow/impedance method. Results demonstrated a statistically significant correlation (r>0.66, p<0.0001) for Reflection Magnitude (RM) and Reflection Index (RI) for the multi-gaussian approach against the triangular flow method for 105 virtual subjects. The range of RM was from 0.35 to 0.97 (RI: 27.53% to 49.29%). This method proves to be a technique for evaluating reflection parameters if only a single pulse measurement is available from any artery.Clinical Relevance- This simulation study supplements the evidence for wave reflections. It provides a new method to study wave reflections using only a single pulse waveform without the need for any measured or modelled flow.

Evaluation of Vascular Pulse Contour Indices over the Physiological Blood Pressure Ranges in an Anesthetized Porcine Model

A series of physiological measures can be assessed from the arterial pulse waveform, which is beneficial for cardiovascular health diagnosis, monitoring, and decision making. In this work, we have investigated the variations in regional pulse wave velocity (PWVR) and other pulse waveform indexes such as reflected wave transit time (RWTT), augmentation index (Alx), ejection duration index (ED), and subendocardial viability ratio (SEVR) with blood pressure (BP) parameters and heartrate on a vasoconstrictor drug-induced porcine model. Two healthy female (nulliparous and non-pregnant) Sus scrofa swine (~ 80 kg) was used for the experimental study. The measurement system consists of a catheter-based system with two highly accurate pressure catheters placed via the sheath at the femoral and carotid artery for acquiring and recording the pressure waveforms. The pulse waveform indexes were extracted from these recorded waveforms. Results from the pulse contour analysis of these waveforms demonstrated that Phenylephrine, as a post-synaptic alpha-adrenergic receptor agonist that causes vasoconstriction, produced a significant increment in the carotid BP parameters and heartrate. Due to the drug's effect, the PWVR and SEVR were significantly increased, whereas the RWTT, AIx index and ED index significantly decreased.Clinical Relevance- This experimental study provides the usefulness of the pulse contour analysis and estimation of various pulse waveform indexes for cardiovascular health screening and diagnosis.

Phantom Assessment of an Image-free Ultrasound Technology for Online Local Pulse Wave Velocity Measurement

Cardiovascular community has started clinically adopting the assessment of local stiffness, contrary to the traditionally measured carotid-femoral pulse wave velocity (PWV). Though they offer higher reliability, ultrasound methods require advanced hardware and processing methods to perform real-time measurement of local PWV. This work presents a system and method to perform online PWV measurement in an automated manner. It is a fast image-free ultrasound technology that meets the methodological requirements necessary to measure small orders of local pulse transit, from which PWV is measured. The measurement accuracy and repeatability were assessed via phantom experiments, where the measured transit time-based PWV (PWVTT) was compared against the theoretically calculated PWV from Bramwell-Hill equation (PWVBH). The beat-to-beat variability in the measured PWVTT was within 3%. PWVTT values strongly correlated (r=0.98) with PWVBH, yielding a negligible bias of -0.01 m/s, mean error of 3%, and RMSE of 0.27 m/s. These pilot study results demonstrated the presented system's reliability in yielding online local PWV measurements.

Gaussian-Mixture Modelling of A-Mode Radiofrequency Scans for the Measurement of Arterial Wall Thickness

Measurement of arterial wall thickness is an integral component of vascular properties and health assessment. State-of-the-art automated or semi-automated techniques are majorly applicable to B-mode images and are not available for entry-level in-expensive devices. Considering this, we have earlier developed and validated an image-free (A-mode) ultrasound device, ARTSENS^® for the evaluation of vascular properties. In this work, we present a novel gaussian-mixture modeling-based method to measure arterial wall thickness from A-mode frames, which is readily deployable to the existing technology. The method's performance was assessed based on systematic simulations and controlled phantom experiments. Simulations revealed that the method could be confidently applied to A-mode frames with above-moderate SNR (>15 dB). When applied to A-mode frames acquired from the flow-phantom setup (SNR > 25 dB), the mean error was limited to (2 ± 1%), and RMSE was 19 μm, on comparison with B-mode measurements. The measured and reference wall thickness strongly agreed with each other (r = 0.88, insignificant mean bias = 7 μm, p = 0.16). The proposed method was capable of performing real-time measurements.

Evaluation of Nonlinear Wave Separation Method to Assess Reflection Transit Time: A Virtual Patient Study

Conventional methods to calculate reflection transit time (RTT) is based on pulse counter analysis. An alternative to this approach is separating forward and backward components from a pulse waveform to calculate the RTT. State-of-the-art in wave separation requires simultaneously measured pressure and flow velocity waveforms. Practically, getting a simultaneous measurement from a single arterial site has its limitations, and this has made the translation of wave separation methods to clinical practice difficult. We propose a new method of wave separation analysis that requires only a single pulse waveform measurement using a multi-Gaussian decomposition approach. The novelty of the method is that it does not require any measured or modelled flow velocity waveform. In this method, the pulse waveform is decomposed into the sum of Gaussians and reconstructed based on model criteria. RTT is calculated as the time difference between normalized forward and backward waveform. The method's feasibility in using RTT as a potential surrogate is demonstrated on 105 diverse selections of virtual subjects. The results were statistically significant and had a strong correlation (r>79, p<0.0001) against clinically approved artery stiffness markers such as Peterson's elastic modulus (Ep), pulse wave velocity (PWV), specific stiffness index (β), and arterial compliance (AC). Out of all the elasticity markers, a better correlation was found against AC.Clinical Relevance-This simulation study supplements the evidence for the dependence of pulse wave reflections on arterial stiffness. It provides a new method to study wave reflections using only a single pulse waveform.

High-Framerate A-Mode Ultrasound for Vascular Structural Assessments: In-Vivo Validation in a Porcine Model

Capturing vascular dynamics using ultrasound at a high framerate provided a unique way to track time-dependent and transient physiologic events non-invasively. In this work, we present an A-model high-framerate (500 frames per second) image-free ultrasound system for monitoring vascular structural and material properties. It was developed based on our clinically validated ARTSENS^® technology. Following in-vitro verification on arterial flow phantoms, its measurement accuracy and high-framerate data acquisition and processing were verified in-vivo on 2 anesthetized Sus scrofa swine. Measurements of the carotid artery (the luminal diameter, distension, and wall thickness) obtained using the high-framerate system were comparable to those provided by a clinical-grade reference ultrasound imaging device (absolute error < 4%, < 6.3%, and < 6.6%, respectively). Notably, the morphology of the arterial distension waveforms obtained at high-framerate depicted vital physiological fiduciary points compared to the low-framerate reference waveform. The compression-decompression pattern of the arterial wall was also captured with the high-framerate system, which is challenging with low-framerate ultrasound. Potential applications of these high temporal structural waveforms have also been discussed.

Association of incremental pulse wave velocity with cardiometabolic risk factors

We investigate the association of incremental pulse wave velocity (ΔC; the change in pulse wave velocity over a cardiac cycle) with cardiometabolic risk factors and report the first and (currently) the largest population-level data. In a cross-sectional study performed in a cohort of 1373 general population participants, ΔC was measured using clinically validated ARTSENS devices. There were 455 participants in the metabolic syndrome (MetS) group whose average ΔC was ~ 28.4% higher than that of the non-metabolic syndrome (Non-MetS) group. Females with MetS showed ~ 10.9% elevated average ΔC compared to males of the Non-MetS group. As the number of risk factors increased from 0 to 5, the average ΔC escalated by ~ 55% (1.50 ± 0.52 m/s to 2.33 ± 0.91 m/s). A gradual increase in average ΔC was observed across each decade from the younger (ΔC = 1.53 ± 0.54 m/s) to geriatric (ΔC = 2.34 ± 0.59 m/s) populations. There was also a significant difference in ΔC among the blood pressure categories. Most importantly, ΔC ≥ 1.81 m/s predicted a constellation of ≥ 3 risks with AUC = 0.615, OR = 2.309, and RR = 1.703. All statistical trends remained significant, even after adjusting for covariates. The study provides initial evidence for the potential use of ΔC as a tool for the early detection and screening of vascular dysfunction, which opens up avenues for active clinical and epidemiological studies. Further investigations are encouraged to confirm and establish the causative mechanism for the reported associations.

VerSe: A Vertebrae labelling and segmentation benchmark for multi-detector CT images

Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision support systems for diagnosis, surgery planning, and population-based analysis of spine and bone health. However, designing automated algorithms for spine processing is challenging predominantly due to considerable variations in anatomy and acquisition protocols and due to a severe shortage of publicly available data. Addressing these limitations, the Large Scale Vertebrae Segmentation Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020, with a call for algorithms tackling the labelling and segmentation of vertebrae. Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel level by a human-machine hybrid algorithm (https://osf.io/nqjyw/, https://osf.io/t98fz/). A total of 25 algorithms were benchmarked on these datasets. In this work, we present the results of this evaluation and further investigate the performance variation at the vertebra level, scan level, and different fields of view. We also evaluate the generalisability of the approaches to an implicit domain shift in data by evaluating the top-performing algorithms of one challenge iteration on data from the other iteration. The principal takeaway from VerSe: the performance of an algorithm in labelling and segmenting a spine scan hinges on its ability to correctly identify vertebrae in cases of rare anatomical variations. The VerSe content and code can be accessed at: https://github.com/anjany/verse.

IQVision: An Image-Based Evaluation Tool for Quantitative Lateral Flow Immunoassay Kits

The development of quantitative lateral flow immunoassay test strips involves a lot of research from kit manufacturers’ standpoint. Kit providers need to evaluate multiple parameters, including the location of test regions, sample flow speed, required sample volumes, reaction stability time, etc. A practical visualization tool assisting manufacturers in this process is very much required for the design of more sensitive and reliable quantitative LFIA test strips. In this paper, we present an image-based quantitative evaluation tool determining the practical functionality of fluorescence-labelled LFIA test cartridges. Image processing-based algorithms developed and presented in this paper provide a practical analysis of sample flow rates, reaction stability times of samples under test, and detect any abnormalities in test strips. Evaluation of the algorithm is done with Glycated Hemoglobin (HbA1C) and Vitamin D test cartridges. Practical sample flow progress for HbA1C test cartridges is demonstrated. The reaction stability time of HbA1C test samples is measured to be 12 min, while that of Vitamin D test samples is 24 min. Experimental evaluation of the abnormality detection algorithm is carried out, and sample flow abnormalities are detected with 100% accuracy while membrane irregularities are detected with 96% accuracy.

Comparative study of silicone membrane simulator and animal eye models for sub-Tenon's block

To compare and assess silicone membrane-based sub-Tenon's block (STB) simulator and animal eye model (goat's eye) for practicing STB in terms of anatomical similarity and feel of texture of eye layers. The study included 34 participants (26 learners and 8 consultants) from tertiary ophthalmic centres. The participants were divided into groups A and B. Group A performed STB on the goat's eyes before using the silicone membrane simulator. Group B performed STB on the simulator and further proceeded to the goat's eye. Participants had to rate the anatomical similarity and feel of the texture for the simulator model on a scale of 0-10 and share their preference between the two models. In group A, the scores given to the simulator model and the feel of texture of layers were 8.05 ± 0.88 and 7.97 ± 1.07, respectively, and the scores given to the animal model and the feel of texture of layers were 8.11 ± 0.97 and 8.21 ± 0.88, respectively. Group B participants scored the simulator model and feel of texture of layers with 8.13 ± 0.95 and 8.25 ± 0.99, respectively. Overall, 89% participants preferred the simulator; the reasons included ease of usage, helpful warning system, absence of biological waste, and facility for repeatable training. The study validated anatomical accuracy, preference, and ability of usage of the STB simulator. For broader usage, further study involving higher number of participants is recommended.

Fast body part segmentation and tracking of neonatal video data using deep learning

Photoplethysmography imaging (PPGI) for non-contact monitoring of preterm infants in the neonatal intensive care unit (NICU) is a promising technology, as it could reduce medical adhesive-related skin injuries and associated complications. For practical implementations of PPGI, a region of interest has to be detected automatically in real time. As the neonates' body proportions differ significantly from adults, existing approaches may not be used in a straightforward way, and color-based skin detection requires RGB data, thus prohibiting the use of less-intrusive near-infrared (NIR) acquisition. In this paper, we present a deep learning-based method for segmentation of neonatal video data. We augmented an existing encoder-decoder semantic segmentation method with a modified version of the ResNet-50 encoder. This reduced the computational time by a factor of 7.5, so that 30 frames per second can be processed at 960 × 576 pixels. The method was developed and optimized on publicly available databases with segmentation data from adults. For evaluation, a comprehensive dataset consisting of RGB and NIR video recordings from 29 neonates with various skin tones recorded in two NICUs in Germany and India was used. From all recordings, 643 frames were manually segmented. After pre-training the model on the public adult data, parts of the neonatal data were used for additional learning and left-out neonates are used for cross-validated evaluation. On the RGB data, the head is segmented well (82% intersection over union, 88% accuracy), and performance is comparable with those achieved on large, public, non-neonatal datasets. On the other hand, performance on the NIR data was inferior. By employing data augmentation to generate additional virtual NIR data for training, results could be improved and the head could be segmented with 62% intersection over union and 65% accuracy. The method is in theory capable of performing segmentation in real time and thus it may provide a useful tool for future PPGI applications. Graphical Abstract This work presents the development of a customized, real-time capable Deep Learning architecture for segmenting of neonatal videos recorded in the intensive care unit. In addition to hand-annotated data, transfer learning is exploited to improve performance.

Demonstration of Pressure-Dependent Inter and Intra-Cycle Variations in Local Pulse Wave Velocity Using Excised Bovine Carotid Artery

Pulse wave velocity (PWV) is a function of the artery's material property, and its incremental nature in elastic modulus led to the concept of incremental PWV. Recent advancements in technology paved the way for reliable measurement of the variation in PWV within a cardiac cycle. This change in PWV has shown its potential as a biomarker for advanced cardiovascular diagnostics, screening, and has recently started using as a vascular screening tool and medical device development. In this work, we have demonstrated the concept of inter and intra-cycle variations of PWV with pressure using an excised bovine carotid artery. Results demonstrated that local PWV measured at the foot of the waveform followed the same trend as of the pressure. As the pressure level was increased to 68% across the cycles, resulting PWV increased up to 81%. An exponential PWV-Pressure relationship was obtained, in agreement with the widely used models. The incremental nature of PWV was recorded in a reflection-free region of the pressure pulse wave. This was further demonstrated in continuous pulse cycles with varying pressure ranges, by comparing the PWV values at two fiduciary points selected in the upstroke of the pressure wave. On average, a 48.11% increase in PWV was observed for 31.04% increase in pressure between the selected fiducial points within a pulse cycle. The article concludes, highlighting the clinical significance of incremental PWV.Clinical Relevance- This experimental study supplements the evidence for the incremental nature of PWV within a cardiac cycle, which has the potential for being a biomarker for advanced cardiovascular screening and diagnostics.

MRI Super-Resolution using Laplacian Pyramid Convolutional Neural Networks with Isotropic Undecimated Wavelet Loss

High spatial resolution of Magnetic Resonance images (MRI) provide rich structural details to facilitate accurate diagnosis and quantitative image analysis. However the long acquisition time of MRI leads to patient discomfort and possible motion artifacts in the reconstructed image. Single Image Super-Resolution (SISR) using Convolutional Neural networks (CNN) is an emerging trend in biomedical imaging especially Magnetic Resonance (MR) image analysis for image post processing. An efficient choice of SISR architecture is required to achieve better quality reconstruction. In addition, a robust choice of loss function together with the domain in which these loss functions operate play an important role in enhancing the fine structural details as well as removing the blurring effects to form a high resolution image. In this work, we propose a novel combined loss function consisting of an L1 Charbonnier loss function in the image domain and a wavelet domain loss function called the Isotropic Undecimated Wavelet loss (IUW loss) to train the existing Laplacian Pyramid Super-Resolution CNN. The proposed loss function was evaluated on three MRI datasets - privately collected Knee MRI dataset and the publicly available Kirby21 brain and iSeg infant brain datasets and on benchmark SISR datasets for natural images. Experimental analysis shows promising results with better recovery of structure and improvements in qualitative metrics.