Deep learning has recently yielded impressive gains in retinal vessel segmentation. However, state-of-the-art methods tend to be conservative, favoring precision over recall. Thus, they tend to ...under-segment faint vessels, underestimate the width of thicker vessels, or even miss entire vessels. To address this limitation, we propose a stochastic training scheme for deep neural networks that robustly balances precision and recall. First, we train our deep networks with dynamic class weights in the loss function that fluctuate during each training iteration. This stochastic approach–which we believe is applicable to many other machine learning problems–forces the network to learn a balanced classification. Second, we decouple the segmentation process into two steps. In the first half of our pipeline, we estimate the likelihood of every pixel and then use these likelihoods to segment pixels that are clearly vessel or background. In the latter part of our pipeline, we use a second network to classify the ambiguous regions in the image. Our proposed method obtained state-of-the-art results on five retinal datasets—DRIVE, STARE, CHASE-DB, AV-WIDE, and VEVIO—by learning a robust balance between false positive and false negative rates. Our novel training paradigm makes a neural network more robust to inter-sample differences in class ratios, which we believe will prove particularly effective for settings with sparse training data, such as medical image analysis. In addition, we are the first to report segmentation results on the AV-WIDE dataset, and we have made the ground-truth annotations for this dataset publicly available. An implementation of this work can be found at https://github.com/sraashis/deepdyn.
Methicillin-resistant Staphylococcus aureus (MRSA) is a significant health threat and public burden worldwide, particularly in developing countries, including Nepal, due to its low healthcare ...standards and irrational use of antibiotics. It is evident that MRSA strains are frequently detected in Nepalese hospitals; however, they remain underreported. Therefore, to provide a comprehensive and clear understanding of MRSA infection at the national level, this systematic review and meta-analysis evaluated the prevalence and antimicrobial susceptibility patterns of MRSA in Nepal.
PubMed, EMBASE, Cochrane CENTRAL, Google scholar, and Nepalese databases were searched for studies published between 1st January 2008 and 31st August 2020. A total of 26 original articles were selected for quantitative analysis. Data extraction was accomplished by three authors independently and meta-analysis was performed using MedCalc Version 19.5.1 and Comprehensive Meta-Analysis (CMA) software v.3.0.
The pooled prevalence of MRSA infections among 5951 confirmed S. aureus isolates was 38.2% (95% CI, 31.4%–45.2%). We found a significant heterogeneity (I2 = 96.7% for resistance proportion), and no evidence of publication bias (p = 0.256) among studies. MRSA strains showed a high level of resistance to beta-lactam antibiotics and the highest susceptibility profile was noted in vancomycin 98.0% followed by chloramphenicol 91.0%.
The analysis revealed that the overall MRSA burden in Nepal is considerably high and the prevalence of MRSA infections is in the increasing trend. Sound legislation, definite antibiotic policy, and implementations of control interventions are indispensable for tackling MRSA infection and antimicrobial resistance as a whole.
The first COVID‐19 case in Nepal was reported on January 23, 2020. Then infection, then, started to spread gradually, and October marked the most devastating increase in COVID‐19 cases of the year ...2020. Compared with the October 2020 peak in Nepal, the May 2021 peak of COVID‐19 observed 2‐ and 10‐fold rise in new cases and deaths per day, respectively. Given that this surprising increase in the death rate was not observed in other countries, this study analyzed the COVID‐19 case fatality rates between the two peaks in Nepal. We found an increase in death rates among younger adults and people without comorbidities.
The world has faced huge negative effects from the COVID-19 pandemic between early 2020 and late 2021. Each country has implemented a range of preventive measures to minimize the risk during the ...COVID-19 pandemic. This study assessed the COVID-19-related fear, risk perception, and preventative behavior during the nationwide lockdown due to COVID-19 in Nepal. In a cross-sectional study, conducted in mid-2021 during the nationwide lockdown in Nepal, a total of 1484 individuals completed measures on fear of COVID-19, COVID-19 risk perception, and preventive behavior. A multiple linear regression analysis was used to identify factors associated with COVID-19 fear. The results revealed significant differences in the fear of COVID-19 in association with the perceived risk of COVID-19 and preventive behaviors. Age, risk perception, preventive behavior, and poor health status were significantly positively related to fear of COVID-19. Perceived risk and preventive behaviors uniquely predicted fear of COVID-19 over and above the effects of socio-demographic variables. Being female and unmarried were the significant factors associated with fear of COVID-19 among study respondents. Higher risk perception, poor health status, and being female were strong factors of increased fear of COVID-19. Targeted interventions are essential to integrate community-level mental health care for COVID-19 resilience.
Accurate measurements of abnormalities like Artery-Vein ratio and tortuosity in fundus images is an actively researched task. Most of the research seems to compute such features independently. ...However, in this work, we have devised a fully automated technique to measure any vascular abnormalities. This paper is a follow-up paper on vessel topology estimation and extraction, we use the extracted topology to perform A-V state-of-the-art Artery-Vein classification, AV ratio calculation, and vessel tortuosity measurement, all fully automated. Existing techniques tend to only work on the partial region, but we extract the complete vascular structure. We have shown the usability of this topology by extracting two of the most important vascular features; Artery-Vein ratio, and vessel tortuosity.
We present a fully automatic, graph-based technique for extracting the retinal vascular topology -- that is, how different vessels are connected to each other -- given a single color fundus image. ...Determining this connectivity is very challenging because vessels cross each other in a 2D image, obscuring their true paths. We quantitatively validated the usefulness of our extraction method by using it to achieve comparable state-of-the-art results in retinal artery-vein classification. Our proposed approach works as follows: We first segment the retinal vessels using our previously developed state-of-the-art segmentation method. Then, we estimate an initial graph from the extracted vessels and assign the most likely blood flow to each edge. We then use a handful of high-level operations (HLOs) to fix errors in the graph. These HLOs include detaching neighboring nodes, shifting the endpoints of an edge, and reversing the estimated blood flow direction for a branch. We use a novel cost function to find the optimal set of HLO operations for a given graph. Finally, we show that our extracted vascular structure is correct by propagating artery/vein labels along the branches. As our experiments show, our topology-based artery-vein labeling achieved state-of-the-art results on three datasets: DRIVE, AV-WIDE, and INSPIRE. We also performed several ablation studies to separately verify the importance of the segmentation and AV labeling steps of our proposed method. These ablation studies further confirmed that our graph extraction pipeline correctly models the underlying vascular anatomy.
Segmenting the retinal vasculature entails a trade-off between how much of
the overall vascular structure we identify vs. how precisely we segment
individual vessels. In particular, state-of-the-art ...methods tend to
under-segment faint vessels, as well as pixels that lie on the edges of thicker
vessels. Thus, they underestimate the width of individual vessels, as well as
the ratio of large to small vessels. More generally, many crucial
bio-markers---including the artery-vein (AV) ratio, branching angles, number of
bifurcation, fractal dimension, tortuosity, vascular length-to-diameter ratio
and wall-to-lumen length---require precise measurements of individual vessels.
To address this limitation, we propose a novel, stochastic training scheme for
deep neural networks that better classifies the faint, ambiguous regions of the
image. Our approach relies on two key innovations. First, we train our deep
networks with dynamic weights that fluctuate during each training iteration.
This stochastic approach forces the network to learn a mapping that robustly
balances precision and recall. Second, we decouple the segmentation process
into two steps. In the first half of our pipeline, we estimate the likelihood
of every pixel and then use these likelihoods to segment pixels that are
clearly vessel or background. In the latter part of our pipeline, we use a
second network to classify the ambiguous regions in the image. Our proposed
method obtained state-of-the-art results on five retinal datasets---DRIVE,
STARE, CHASE-DB, AV-WIDE, and VEVIO---by learning a robust balance between
false positive and false negative rates. In addition, we are the first to
report segmentation results on the AV-WIDE dataset, and we have made the
ground-truth annotations for this dataset publicly available.
The optic disc is a crucial diagnostic feature in the eye since changes to its physiognomy is correlated with the severity of various ocular and cardiovascular diseases. While identifying the bulk of ...the optic disc in a color fundus image is straightforward, accurately segmenting its boundary at the pixel level is very challenging. In this work, we propose disc-centered patch augmentation (DCPA) -- a simple, yet novel training scheme for deep neural networks -- to address this problem. DCPA achieves state-of-the-art results on full-size images even when using small neural networks, specifically a U-Net with only 7 million parameters as opposed to the original 31 million. In DCPA, we restrict the training data to patches that fully contain the optic nerve. In addition, we also train the network using dynamic cost functions to increase its robustness. We tested DCPA-trained networks on five retinal datasets: DRISTI, DRIONS-DB, DRIVE, AV-WIDE, and CHASE-DB. The first two had available optic disc ground truth, and we manually estimated the ground truth for the latter three. Our approach achieved state-of-the-art F1 and IOU results on four datasets (95 % F1, 91 % IOU on DRISTI; 92 % F1, 84 % IOU on DRIVE; 83 % F1, 71 % IOU on AV-WIDE; 83 % F1, 71 % IOU on CHASEDB) and competitive results on the fifth (95 % F1, 91 % IOU on DRIONS-DB), confirming its generality. Our open-source code and ground-truth annotations are available at: https://github.com/saeidmotevali/fundusdisk
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