ResDUnet: Residual Dilated UNet for Left Ventricle Segmentation from Echocardiographic Images

Echocardiography is the modality of choice for the assessment of left ventricle function. Left ventricle is responsible for pumping blood rich in oxygen to all body parts. Segmentation of this chamber from echocardiographic images is a challenging task, due to the ambiguous boundary and inhomogeneous intensity distribution. In this paper we propose a novel deep learning model named ResDUnet. The model is based on U-net incorporated with dilated convolution, where residual blocks are employed instead of the basic U-net units to ease the training process. Each block is enriched with squeeze and excitation unit for channel-wise attention and adaptive feature re-calibration. To tackle the problem of left ventricle shape and size variability, we chose to enrich the process of feature concatenation in U-net by integrating feature maps generated by cascaded dilation. Cascaded dilation broadens the receptive field size in comparison with traditional convolution, which allows the generation of multi-scale information which in turn results in a more robust segmentation. Performance measures were evaluated on a publicly available dataset of 500 patients with large variability in terms of quality and patients pathology. The proposed model shows a dice similarity increase of 8.4% when compared to deeplabv3 and 1.2% when compared to the basic U-net architecture. Experimental results demonstrate the potential use in clinical domain

Learning spatiotemporal features for esophageal abnormality detection from endoscopic videos

Esophageal cancer is categorized as a type of disease with a high mortality rate. Early detection of esophageal abnormalities (i.e. precancerous and early can- cerous) can improve the survival rate of the patients. Re- cent deep learning-based methods for selected types of esophageal abnormality detection from endoscopic images have been proposed. However, no methods have been introduced in the literature to cover the detection from endoscopic videos, detection from challenging frames and detection of more than one esophageal abnormality type. In this paper, we present an efficient method to automat- ically detect different types of esophageal abnormalities from endoscopic videos. We propose a novel 3D Sequen- tial DenseConvLstm network that extracts spatiotemporal features from the input video. Our network incorporates 3D Convolutional Neural Network (3DCNN) and Convolu- tional Lstm (ConvLstm) to efficiently learn short and long term spatiotemporal features. The generated feature map is utilized by a region proposal network and ROI pooling layer to produce a bounding box that detects abnormal- ity regions in each frame throughout the video. Finally, we investigate a post-processing method named Frame Search Conditional Random Field (FS-CRF) that improves the overall performance of the model by recovering the missing regions in neighborhood frames within the same clip. We extensively validate our model on an endoscopic video dataset that includes a variety of esophageal ab- normalities. Our model achieved high performance using different evaluation metrics showing 93.7% recall, 92.7% precision, and 93.2% F-measure. Moreover, as no results have been reported in the literature for the esophageal abnormality detection from endoscopic videos, to validate the robustness of our model, we have tested the model on a publicly available colonoscopy video dataset, achieving the polyp detection performance in a recall of 81.18%, precision of 96.45% and F-measure 88.16%, compared to the state-of-the-art results of 78.84% recall, 90.51% preci- sion and 84.27% F-measure using the same dataset. This demonstrates that the proposed method can be adapted to different gastrointestinal endoscopic video applications with a promising performance

Predicting epileptic seizures with a stacked long short-term memory network

Despite advancements, seizure detection algorithms are trained using only the data recorded frompast epileptic seizures. This one-dimensional approach has led to an excessive false detection rate,where common movements are incorrectly classified. Therefore, a new method of detection isrequired that can distinguish between the movements observed during a generalized tonic-clonic(GTC) seizure and common everyday activities. For this study, eight healthy participants and twodiagnosed with epilepsy simulated a series of activities that share a similar set of spatialcoordinates with an epileptic seizure. We then trained a stacked, long short-term memory (LSTM)network to classify the different activities. Results show that our network successfullydifferentiated the types of movement, with an accuracy score of 94.45%. These findings present amore sophisticated method of detection that correlates a wearers movement against 12 seizurerelated activities prior to formulating a prediction

Resolving the sign conflict problem for hp–hexahedral Nédélec elements with application to eddy current problems

The eddy current approximation of Maxwell’s equations is relevant for Magnetic Induction Tomography (MIT), which is a practical system for the detection of conducting inclusions from measurements of mutual inductance with both industrial and clinical applications. An MIT system produces a conductivity image from the measured fields by solving an inverse problem computationally. This is typically an iterative process, which requires the forward solution of a Maxwell’s equations for the electromagnetic fields in and around conducting bodies at each iteration. As the (conductivity) images are typically described by voxels, a hexahedral finite element grid is preferable for the forward solver. Low order Nédélec (edge element) discretisations are generally applied, but these require dense meshes to ensure that skin effects are properly captured. On the other hand, hp–Nédélec finite elements can ensure the skin effects in conducting components are accurately captured, without the need for dense meshes and, therefore, offer possible advantages for MIT. Unfortunately, the hierarchic nature of hp–Nédélec basis functions introduces edge and face parameterisations leading to sign conflict issues when enforcing tangential continuity between elements. This work describes a procedure for addressing this issue on general conforming hexahedral meshes and an implementation of a hierarchic hp–Nédélec finite element basis within the deal.II finite element library. The resulting software is used to simulate Maxwell forward problems, including those set on multiply connected domains, to demonstrate its potential as an MIT forward solver

Accelerating magnetic induction tomography‐based imaging through heterogeneous parallel computing

Magnetic Induction Tomography (MIT) is a non‐invasive imaging technique, which has applications in both industrial and clinical settings. In essence, it is capable of reconstructing the electromagnetic parameters of an object from measurements made on its surface. With the exploitation of parallelism, it is possible to achieve high quality inexpensive MIT images for biomedical applications on clinically relevant time scales. In this paper we investigate the performance of different parallel implementations of the forward eddy current problem, which is the main computational component of the inverse problem through which measured voltages are converted into images. We show that a heterogeneous parallel method that exploits multiple CPUs and GPUs can provide a high level of parallel scaling, leading to considerably improved runtimes. We also show how multiple GPUs can be used in conjunction with deal.II, a widely‐used open source finite element library

Energy Efficiency Improvements in Dry Drilling with Optimised Diamond-Like Carbon Coating

We demonstrate enhancements of performance and energy efficiency of cutting tools by deposition of diamond-like carbon (DLC) coatings on machine parts. DLC was deposited on steel drill bits, using plasma enhanced chemical vapour deposition (PECVD) with the acetylene precursor diluted with argon, to produce a surface with low friction and low wear rate. Drill bit performance in dry drilling of aluminium was quantified by analysis of power consumption and swarf flow. Optimised deposition conditions produced drill bits with greatly enhanced performance over uncoated drill bits, showing a 25% reduction in swarf clogging, a 36% reduction in power consumption and a greater than five-fold increase in lifetime. Surface analysis with scanning electron microscopy shows that DLC coated drills exhibit much lower aluminium build up on the trailing shank of the drill, enhancing the anti-adhering properties of the drill and reducing heat generation during operation, resulting in the observed improvements in efficiency. Variation of drilling efficiency with argon dilution of precursor is related to changes in the microstructure of the DLC coating

Esophageal Abnormality Detection Using DenseNet Based Faster R-CNN With Gabor Features

Early detection of esophageal abnormalities can help in preventing the progression of the disease into later stages. During esophagus examination, abnormalities are often overlooked due to the irregular shape, variable size, and the complex surrounding area which requires a significant effort and experience. In this paper, a novel deep learning model which is based on faster region-based convolutional neural network (Faster R-CNN) is presented to automatically detect abnormalities in the esophagus from endoscopic images. The proposed detection system is based on a combination of Gabor handcrafted features with the CNN features. The densely connected convolutional networks (DenseNets) architecture is embraced to extract the CNN features providing a strengthened feature propagation between the layers and alleviate the vanishing gradient problem. To address the challenges of detecting abnormal complex regions, we propose fusing extracted Gabor features with the CNN features through concatenation to enhance texture details in the detection stage. Our newly designed architecture is validated on two datasets (Kvasir and MICCAI 2015). Regarding the Kvasir, the results show an outstanding performance with a recall of 90.2% and a precision of 92.1% with a mean of average precision (mAP) of 75.9%. While for the MICCAI 2015 dataset, the model is able to surpass the state-of-the-art performance with 95% recall and 91% precision with mAP value of 84%. The experimental results demonstrate that the system is able to detect abnormalities in endoscopic images with good performance without any human intervention

Machine learning models for stream-level predictions using readings from satellite and ground gauging stations

While the accuracy of flood predictions is likely to improve with increasing gauging station networks and robust radar coverage, challenges arise when such sources are spatially limited [1]. For instance, severe rainfall events in the UK come mostly from the North Atlantic area where gauges are ineffective and radar instruments are limited to it 250km range. In these cases, NASA’s IMERG is an alternative source of precipitation estimates offering global coverage with 0.1-degree spatial resolution at 30-minute intervals. The IMERG estimates for the UK’s case can offer an opportunity to extend the zone of rainfall detection beyond the radar range and increase lead time on flood risk predictions [2]. This study investigates the ability of machine learning (ML) models to capture the patterns between rainfall and stream level, observed during 20 years in the River Crane in the UK. To compare performances, the models use two sources of rainfall data as input for stream level prediction, the IMERG final run estimates and rain gauge readings. Among the three IMERG products (early, late, and final), the final run was selected for this study due to its higher accuracy in rainfall estimates. The rainfall data was retrieved from rain gauges and the pixel in the IMERG dataset grid closest to the point where stream level readings were taken. These datasets were assessed regarding their correlation with stream level using cross-correlation analysis. The assessment revealed a small variance in the lags and correlation coefficients between the stream-level and the IMERG dataset compared to the lags and coefficients found between stream-level and the gauge’s datasets. To evaluate and compare the performance of each dataset as input in ML models for stream-level predictions, three models were selected: NARX, LSTM, and GRU. Both inputs performed well in the NARX model and produced stream-level predictions of high precision with MSE equal to 1.5×10-5 while using gauge data and 1.9×10-5 for the IMERG data. The LSTM model also produced good predictions, however, the MSE was considerably higher, MSE of 1.8×10-3 for gauging data and 4.9×10-3 for IMERG data. Similar performance was observed in the GRU predictions with MSE of 1.9×10-3 for gauging data and 5.6×10-3 for IMERG. Nonetheless, the results of all models are within acceptable ranges of efficacy confirming the applicability of ML models on stream-level prediction based just on rainfall and stream-level information. More importantly, the small difference between the results obtained from IMERG estimates and gauging data seems promising for future tests of IMERG rainfall data sourced from other pixels of the dataset’s grid and to explore the potential for increased lead time of predictions.Peer reviewe

Optimising oceanic rainfall estimates for increased lead time of stream level forecasting: A case study of GPM IMERG estimates application in the UK

Among the three main rainfall data sources (rain gauge stations, rainfall radar stations and weather satellites), satellites are often the most appropriate for longer lead times in real-time flood forecasting [1]. This is particularly relevant in the UK, where severe rainfall events often originate over the Atlantic Ocean, distant from land-based instruments although it can also limit the effectiveness of satellite data for long-term predictions [2]. The Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) estimates can be used as an alternative source for rainfall information in real-time flood forecasting models. However, the challenge lies in monitoring the vast oceanic region around the UK and integrating this extensive data into hydrological or data-driven models, which presents computational and time constraints. Identifying key monitoring area for obtaining these estimates is essential to address these challenges and to effectively use this use for water level forecasting in urban drainage systems (UDS). This study introduced an optimised data-driven model for streamline the collection and use of GPM IMERG rainfall estimates for water level forecasting in UDS. The model’s effectiveness was demonstrated using a 20-year satellite data set from the Atlantic Ocean, west of the UK, focusing on water level forecasting for a specific UDS point in London. This data helped identify the most probable path of rainfall from the Atlantic that impacts UDS water levels. We conducted a cross-correlation analysis between the water level records and each IMERG data pixel within the selected oceanic area. The analysis successfully pinpointed the most influential rainfall points/pixels along the Atlantic path and their respective lag times between rainfall occurrence and water level changes at any satellite-monitored point until it reaches the mainland and joins the river system. This research enhances understanding of long-distance rainfall patterns while optimising the use of GPM IMERG data. It also aids in reducing data volume and processing time for stream-level forecasting models, aiming for longer lead times.Peer reviewe

A critical review of digital technology innovations for early warning of water-related disease outbreaks associated with climatic hazards

Water-related climatic disasters pose a significant threat to human health due to the potential of disease outbreaks, which are exacerbated by climate change. Therefore, it is crucial to predict their occurrence with sufficient lead time to allow for contingency plans to reduce risks to the population. Opportunities to address this challenge can be found in the rapid evolution of digital technologies. This study conducted a critical analysis of recent publications investigating advanced technologies and digital innovations for forecasting, alerting, and responding to water-related extreme events, particularly flooding, which is often linked to disaster-related disease outbreaks. The results indicate that certain digital innovations, such as portable and local sensors integrated with web-based platforms are new era for predicting events, developing control strategies and establishing early warning systems. Other technologies, such as augmented reality, virtual reality, and social media, can be more effective for monitoring flood spread, disseminating before/during the event information, and issuing warnings or directing emergency responses. The study also identified that the collection and translation of reliable data into information can be a major challenge for effective early warning systems and the adoption of digital innovations in disaster management. Augmented reality, and digital twin technologies should be further explored as valuable tools for better providing of communicating complex information on disaster development and response strategies to a wider range of audiences, particularly non-experts. This can help to increase community engagement in designing and operating effective early warning systems that can reduce the health impact of climatic disasters