Detection of Ulcerative Colitis Severity and Enhancement of Informative Frame Filtering Using Texture Analysis in Colonoscopy Videos

There are several types of disorders that affect our colon’s ability to function properly such as colorectal cancer, ulcerative colitis, diverticulitis, irritable bowel syndrome and colonic polyps. Automatic detection of these diseases would inform the endoscopist of possible sub-optimal inspection during the colonoscopy procedure as well as save time during post-procedure evaluation. But existing systems only detects few of those disorders like colonic polyps. In this dissertation, we address the automatic detection of another important disorder called ulcerative colitis. We propose a novel texture feature extraction technique to detect the severity of ulcerative colitis in block, image, and video levels. We also enhance the current informative frame filtering methods by detecting water and bubble frames using our proposed technique. Our feature extraction algorithm based on accumulation of pixel value difference provides better accuracy at faster speed than the existing methods making it highly suitable for real-time systems. We also propose a hybrid approach in which our feature method is combined with existing feature method(s) to provide even better accuracy. We extend the block and image level detection method to video level severity score calculation and shot segmentation. Also, the proposed novel feature extraction method can detect water and bubble frames in colonoscopy videos with very high accuracy in significantly less processing time even when clustering is used to reduce the training size by 10 times

An Online Learning System for Wireless Charging Alignment using Surround-view Fisheye Cameras

Electric Vehicles are increasingly common, with inductive chargepads being considered a convenient and efficient means of charging electric vehicles. However, drivers are typically poor at aligning the vehicle to the necessary accuracy for efficient inductive charging, making the automated alignment of the two charging plates desirable. In parallel to the electrification of the vehicular fleet, automated parking systems that make use of surround-view camera systems are becoming increasingly popular. In this work, we propose a system based on the surround-view camera architecture to detect, localize, and automatically align the vehicle with the inductive chargepad. The visual design of the chargepads is not standardized and not necessarily known beforehand. Therefore, a system that relies on offline training will fail in some situations. Thus, we propose a self-supervised online learning method that leverages the driver's actions when manually aligning the vehicle with the chargepad and combine it with weak supervision from semantic segmentation and depth to learn a classifier to auto-annotate the chargepad in the video for further training. In this way, when faced with a previously unseen chargepad, the driver needs only manually align the vehicle a single time. As the chargepad is flat on the ground, it is not easy to detect it from a distance. Thus, we propose using a Visual SLAM pipeline to learn landmarks relative to the chargepad to enable alignment from a greater range. We demonstrate the working system on an automated vehicle as illustrated in the video at https://youtu.be/_cLCmkW4UYo. To encourage further research, we will share a chargepad dataset used in this work.Comment: Accepted for publication at IEEE Transactions on Intelligent Transportation System

Enhancing Informative Frame Filtering by Water and Bubble Detection in Colonoscopy Videos

Colonoscopy has contributed to a marked decline in the number of colorectal cancer related deaths. However, recent data suggest that there is a significant (4-12%) miss-rate for the detection of even large polyps and cancers. To address this, we have been investigating an ‘automated feedback system’ which informs the endoscopist of possible sub-optimal inspection during colonoscopy. A fundamental step of this system is to distinguish non-informative frames from informative ones. Existing methods for this cannot classify water/bubble frames as non-informative even though they do not carry any useful visual information of the colon mucosa. In this paper, we propose a novel texture feature based on accumulation of pixel differences, which can detect water and bubble frames with very high accuracy with significantly less processing time. The experimental results show the proposed feature can achieve more than 93% overall accuracy in almost half of the processing time the existing methods take

Genomic epidemiology of SARS-CoV-2 in a UK university identifies dynamics of transmission

AbstractUnderstanding SARS-CoV-2 transmission in higher education settings is important to limit spread between students, and into at-risk populations. In this study, we sequenced 482 SARS-CoV-2 isolates from the University of Cambridge from 5 October to 6 December 2020. We perform a detailed phylogenetic comparison with 972 isolates from the surrounding community, complemented with epidemiological and contact tracing data, to determine transmission dynamics. We observe limited viral introductions into the university; the majority of student cases were linked to a single genetic cluster, likely following social gatherings at a venue outside the university. We identify considerable onward transmission associated with student accommodation and courses; this was effectively contained using local infection control measures and following a national lockdown. Transmission clusters were largely segregated within the university or the community. Our study highlights key determinants of SARS-CoV-2 transmission and effective interventions in a higher education setting that will inform public health policy during pandemics.</jats:p

Super resolution aided multi hazard modeling: Is it possible?

The losses due to natural hazards are very high and show an increasing trend due to climate change; human and economic growth; and unplanned development. The risk due to those hazards can be reduced using multi-hazard risk assessment using hazard, the element at risk and vulnerability data. However, due to the lack of good quality and high-resolution data in developing nations, modelling hazards at infrastructure level is difficult. Deep learning-based Super-Resolution can be a solution to increase the spatial resolution of freely available global datasets. However, no studies exist that produced high-resolution output from globally available DEM data using Super-Resolution to improve the quality of physically based hazard modelling. Furthermore, due to differences in data collection sources and value ranges in DEMs, they cannot be compared in absolute values, and there is a lack of techniques to evaluate the improvement done with Super-Resolution in geoscientific data. Moreover, none of the existing research has trained the Super-Resolution models in one region and applied them to another region. To address these problems, our research aimed to increase the applicability of physically based models in data-poor regions by improving the spatial resolution of globally available datasets by using deep learning-based Super-Resolution. To fulfil our objectives, we selected to work on Digital Elevation Models as the target variable due to its importance in hazard modelling and global availability. We used the two of the most advanced Super-Resolution models (EBRN and ESRGAN), each from different groups of deep learning architecture. These models were trained extensively using high-resolution LiDAR DEM data from Austria. After proving that they perform better than most used interpolation techniques such as bicubic interpolation in the study areas in Austria, they were applied in globally available free datasets in Colombia and Dominica. Furthermore, novel loss function and evaluation metrics were developed to train and evaluate the results focusing on improving DEM data. Furthermore, physically based modelling was used to evaluate the impact of Super-Resolution in multi-hazard modelling. We used 21 different scenarios to test the applicability of Super-Resolution compared to existing interpolation techniques and global commercial data. Each scenario was calibrated for 20 iterations (total 420 iterations, ~5460 CPU hours) in Microsoft Azure, which is the first time that OpenLISEM was used in a cloud computing environment. The results were evaluated in terms of the modelled extent of hazardous processes, the height of flow, and the time of solid and fluid flow to prove the applicability of the Super-Resolution approach. The analysis shows that the use of global DEM data with Super-Resolution processing was able to increase the accuracy of hazard modelling output as compared to DEMs made with existing interpolation techniques. Furthermore, when evaluating derivative DEM products through visual analysis, it is observed that the Super-Resolution has increased the crispness of valley lines and ridgelines in the DEM datasets. However, the specific topographic features that are not present in low-resolution data could not be reconstructed using the Super-Resolution, limiting its use in geomorphological mapping. The applicability of Super-Resolution was tested in multiple locations, and it could prove that the technique resulted in 8-25% improvement in all of the study sites. The results also show that the capacity of both models (EBRN and ESRGAN) is generally very similar. There are a few challenges in calibration, such as the use of Gradient Descent requiring more iterations and the lack of datasets and metrics to compare our results with existing Super-Resolution models. Furthermore, we could not compare our results on multi-hazard modelling to other research because there is no published work using the Super-Resolution in multi-hazard modelling

Bacillus fermentation of soybean : a review

Soybeans in its natural form have a little direct use as a food due to its poor digestibility as well as beany taste and flavour. Fermentation; however, can improve the eating and nutritional qualities of soybeans. Fermented soybean foods have been an intricate part of oriental diet for a long time. Bacillus subtilis dominated traditionally fermented soyfoods have typical taste, texture and aroma which is popular in Asian and African countries. B. subtilis fermentation of soaked and cooked soybeans brings many physico-chemicals and sensory changes that make it highly digestible and nutritious. This paper reviews various facets of B. subtilis fermented traditional foods, properties of fermenting organisms, preparation of such fermented foods, changes in chemical composition and nutritional properties and improving the quality of these foods

Spatial Loss Function for Super-Resolution of Geoscientific Data

Super Resolution is a method for artificially increasing the imaging system's resolution by post processing without having to collect new datasets. It is mostly developed and used in computer graphics by the computer science community for image and video enhancement due to its capacity to add spatial variations in the data and perform better than conventional interpolation methods such as bicubic interpolation been used extensively in the geoscience community. After the advancement of deep learning-based super-resolution methods in the 2010s, it has shown great potential for use in data scare regions where high-resolution geoscientific data are not available, and collection of such data is also not possible due to financial and technical reasons. Even though Super-Resolution is used geospatial data, the loss functions to optimize those models are mostly used as it is from computer vision, and they do not account for the spatial relationship between neighbouring pixels. For example, in the case of elevation data, the relative elevation between two pixels (slope) and their direction (aspect) is more important compared to absolute elevation in the case of geoscientific modelling. However, those relations which must be valid in the ground observation are not well considered in Super-Resolution of Geospatial data. Our research aims to develop a loss function that can respect the spatial relationship between pixel and its neighbours, representing the ground reality. In contrast to existing methods, these new loss functions are better in generating the geospatial datasets that can be further used in geoscientific analysis and modelling approaches rather than mere visualization. The developed loss function is tested with multiple super-resolution models, both generative adversarial networks and the end-to-end models trained with geoscientific data. Our research shows that using spatial relation aware loss function and the super-resolution model can better reconstruct the ground reality even though training them is more complex than using simple mean squared error loss functions. The use of such a novel loss function can also generate better terrain in the case of Digital Elevation Models, which can be observed in the slope and aspect of such datasets

Enhancing Informative Frame Filtering by Water and Bubble Detection in Colonoscopy Videos

Colonoscopy has contributed to a marked decline in the number of colorectal cancer related deaths. However, recent data suggest that there is a significant (4-12%) miss-rate for the detection of even large polyps and cancers. To address this, we have been investigating an ‘automated feedback system’ which informs the endoscopist of possible sub-optimal inspection during colonoscopy. A fundamental step of this system is to distinguish non-informative frames from informative ones. Existing methods for this cannot classify water/bubble frames as non-informative even though they do not carry any useful visual information of the colon mucosa. In this paper, we propose a novel texture feature based on accumulation of pixel differences, which can detect water and bubble frames with very high accuracy with significantly less processing time. The experimental results show the proposed feature can achieve more than 93% overall accuracy in almost half of the processing time the existing methods take.This article is from WorldComp 2015 International Conference Health Informatics and Medical Systems | HIMS'15 : pp. 24-30. Posted with permission.</p