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

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

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

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

Automated segmentation of left ventricle in 2D echocardiography using deep learning

Following the successful application of the U-Net to medical images, there have been different encoder-decoder models proposed as an improvement to the original U-Net for segmenting echocardiographic images. This study aims to examine the performance of the state-of-the-art proposed models claimed to have better accuracies, as well as the original U-Net model by applying them to an independent dataset of patients to segment the endocardium of the Left Ventricle in 2D automatically. The prediction outputs of the models are used to evaluate the performance of the models by comparing the automated results against the expert annotations (gold standard). Our results reveal that the original U-Net model outperforms other models by achieving an average Dice coefficient of 0.92±0.05, and Hausdorff distance of 3.97±0.82

Automated assessment of transthoracic echocardiogram image quality using deep neural networks

Background Standard views in two-dimensional echocardiography are well established but the quality of acquired images are highly dependent on operator skills and are assessed subjectively. This study is aimed at providing an objective assessment pipeline for echocardiogram image quality by defining a new set of domain-specific quality indicators. Consequently, image quality assessment can thus be automated to enhance clinical measurements, interpretation, and real-time optimization. Methods We have developed deep neural networks for the automated assessment of echocardiographic frame which were randomly sampled from 11,262 adult patients. The private echocardiography dataset consists of 33,784 frames, previously acquired between 2010 and 2020. Unlike non-medical images where full-reference metrics can be applied for image quality, echocardiogram's data is highly heterogeneous and requires blind-reference (IQA) metrics. Therefore, deep learning approaches were used to extract the spatiotemporal features and the image's quality indicators were evaluated against the mean absolute error. Our quality indicators encapsulate both anatomical and pathological elements to provide multivariate assessment scores for anatomical visibility, clarity, depth-gain and foreshortedness, respectively. Results The model performance accuracy yielded 94.4%, 96.8%, 96.2%, 97.4% for anatomical visibility, clarity, depth-gain and foreshortedness, respectively. The mean model error of 0.375±0.0052 with computational speed of 2.52 ms per frame (real-time performance) was achieved. Conclusion The novel approach offers new insight to objective assessment of transthoracic echocardiogram image quality and clinical quantification in A4C and PLAX views. Also lays stronger foundations for operator's guidance system which can leverage the learning curve for the acquisition of optimum quality images during transthoracic exam

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

An optimisation-based iterative approach for speckle tracking echocardiography

Speckle tracking is the most prominent technique used to estimate the regional movement of the heart based on echocardiograms. In this study, we propose an optimised-based block matching algorithm to perform speckle tracking iteratively. The proposed technique was evaluated using a publicly available synthetic echocardiographic dataset with known ground-truth from several major vendors and for healthy/ischaemic cases. The results were compared with the results from the classic (standard) two-dimensional block matching. The proposed method presented an average displacement error of 0.57 pixels, while classic block matching provided an average error of 1.15 pixels. When estimating the segmental/regional longitudinal strain in healthy cases, the proposed method, with an average of 0.32 ± 0.53, outperformed the classic counterpart, with an average of 3.43 ± 2.84. A similar superior performance was observed in ischaemic cases. This method does not require any additional ad hoc filtering process. Therefore, it can potentially help to reduce the variability in the strain measurements caused by various post-processing techniques applied by different implementations of the speckle tracking

Doppler assessment of aortic stenosis: a 25-operator study demonstrating why reading the peak velocity is superior to velocity time integral

Aims Measurements with superior reproducibility are useful clinically and research purposes. Previous reproducibility studies of Doppler assessment of aortic stenosis (AS) have compared only a pair of observers and have not explored the mechanism by which disagreement between operators occurs. Using custom-designed software which stored operators’ traces, we investigated the reproducibility of peak and velocity time integral (VTI) measurements across a much larger group of operators and explored the mechanisms by which disagreement arose. Methods and results Twenty-five observers reviewed continuous wave (CW) aortic valve (AV) and pulsed wave (PW) left ventricular outflow tract (LVOT) Doppler traces from 20 sequential cases of AS in random order. Each operator unknowingly measured each peak velocity and VTI twice. VTI tracings were stored for comparison. Measuring the peak is much more reproducible than VTI for both PW (coefficient of variation 10.1 vs. 18.0%; P < 0.001) and CW traces (coefficient of variation 4.0 vs. 10.2%; P < 0.001). VTI is inferior because the steep early and late parts of the envelope are difficult to trace reproducibly. Dimensionless index improves reproducibility because operators tended to consistently over-read or under-read on LVOT and AV traces from the same patient (coefficient of variation 9.3 vs. 17.1%; P < 0.001). Conclusion It is far more reproducible to measure the peak of a Doppler trace than the VTI, a strategy that reduces measurement variance by approximately six-fold. Peak measurements are superior to VTI because tracing the steep slopes in the early and late part of the VTI envelope is difficult to achieve reproducibly