Integration of solar energy and optimized economic dispatch using genetic algorithm: A case-study of Abu Dhabi

© 2017 IEEE. The United Arab Emirates is focusing on cultivating Renewable Energy (RE) to meet its growing power demand. This also brings power planning to the forefront in regards to keen interests in renewable constrained economic dispatch. This paper takes note of UAE's vision in incorporating a better energy mix of Renewable Energy (RE), nuclear, hybrid system along with the existing power plants mostly utilizing natural gas; with further attention for a sound economic dispatch scenario. The paper describes economic dispatch and delves into the usage of Genetic Algorithm to optimize the proposed system of thermal plants and solar systems. The paper explains the problem formulation, describes the system used, and illustrates the results achieved. The aim of the research is in line with the objective function to minimize the total costs of production and to serve the purpose of integrating renewable energy into the traditional power production in UAE. The generation mix scenarios are assessed using genetic algorithm using MATLAB simulation for the optimization problem

Multi-modal brain tumor segmentation via conditional synthesis with Fourier domain adaptation

Accurate brain tumor segmentation is critical for diagnosis and treatment planning, whereby multi-modal magnetic resonance imaging (MRI) is typically used for analysis. However, obtaining all required sequences and expertly labeled data for training is challenging and can result in decreased quality of segmentation models developed through automated algorithms. In this work, we examine the possibility of employing a conditional generative adversarial network (GAN) approach for synthesizing multi-modal images to train deep learning-based neural networks aimed at high-grade glioma (HGG) segmentation. The proposed GAN is conditioned on auxiliary brain tissue and tumor segmentation masks, allowing us to attain better accuracy and control of tissue appearance during synthesis. To reduce the domain shift between synthetic and real MR images, we additionally adapt the low-frequency Fourier space components of synthetic data, reflecting the style of the image, to those of real data. We demonstrate the impact of Fourier domain adaptation (FDA) on the training of 3D segmentation networks and attain significant improvements in both the segmentation performance and prediction confidence. Similar outcomes are seen when such data is used as a training augmentation alongside the available real images. In fact, experiments on the BraTS2020 dataset reveal that models trained solely with synthetic data exhibit an improvement of up to 4% in Dice score when using FDA, while training with both real and FDA-processed synthetic data through augmentation results in an improvement of up to 5% in Dice compared to using real data alone. This study highlights the importance of considering image frequency in generative approaches for medical image synthesis and offers a promising approach to address data scarcity in medical imaging segmentation.</p

Patient and sequence metadata

An excel sheet containing patient characteristics with clinical indications, as well as scan characteristics with image sequences per scanner vendor/model type

Late Fusion U-Net with GAN-Based Augmentation for Generalizable Cardiac MRI Segmentation

Accurate segmentation of the right ventricle (RV) in cardiac magnetic resonance (CMR) images is crucial for ventricular structure and function assessment. However, due to its variable anatomy and ill-defined borders, RV segmentation remains an open problem. While recent advances in deep learning show great promise in tackling these challenges, such methods are typically developed on homogeneous data-sets, not reflecting realistic clinical variation in image acquisition and pathology. In this work, we develop a model, aimed at segmenting all three cardiac structures in a multi-center, multi-disease and multi-view setting, using data provided by the M&amp;Ms-2 challenge. We propose a pipeline addressing various aspects of segmenting heterogeneous data, consisting of heart region detection, augmentation through image synthesis and multi-fusion segmentation. Our extensive experiments demonstrate the importance of different elements of the pipeline, achieving competitive results for RV segmentation in both short-axis and long-axis MR images.</p

Cardiac MR Image Segmentation and Quality Control in the Presence of Respiratory Motion Artifacts Using Simulated Data

In this work, we propose solutions for the two tasks of the CMRxMotion challenge; 1) quality control and 2) image segmentation in the presence of respiratory motion artifacts. We develop a k-space based motion simulation approach to generate cardiac MR images with respiratory motion artifacts on open-source artifact-free data to handle data scarcity. For task 1, a motion-denoising auto-encoder is trained to reconstruct motion-free images from the pairs of images with and without simulated motion. The encoder part of the auto-encoder is used as a feature extractor for a fully-connected classifier. For task 2, an ensemble of modified 2D nn-Unet models is proposed to tackle different aspects of variations in the data with the purpose of improving the robustness of the model to images hampered by respiratory motion artifacts. All proposed models in this paper are trained using the images with simulated motion artifacts. The proposed quality control model achieves a classification accuracy of 0.75 with the Cohen’s kappa coefficient of 0.64 and the ensemble model obtains the mean Dice scores of 0.922, 0.829, and 0.910 respectively for the left ventricle, myocardium, and right ventricle segmentation on the validation set of the CMRxMotion challenge.</p

Reducing segmentation failures in cardiac MRI via late feature fusion and GAN-based augmentation

Cardiac magnetic resonance (CMR) image segmentation is an integral step in the analysis of cardiac function and diagnosis of heart related diseases. While recent deep learning-based approaches in automatic segmentation have shown great promise to alleviate the need for manual segmentation, most of these are not applicable to realistic clinical scenarios. This is largely due to training on mainly homogeneous datasets, without variation in acquisition, which typically occurs in multi-vendor and multi-site settings, as well as pathological data. Such approaches frequently exhibit a degradation in prediction performance, particularly on outlier cases commonly associated with difficult pathologies, artifacts and extensive changes in tissue shape and appearance. In this work, we present a model aimed at segmenting all three cardiac structures in a multi-center, multi-disease and multi-view scenario. We propose a pipeline, addressing different challenges with segmentation of such heterogeneous data, consisting of heart region detection, augmentation through image synthesis and a late-fusion segmentation approach. Extensive experiments and analysis demonstrate the ability of the proposed approach to tackle the presence of outlier cases during both training and testing, allowing for better adaptation to unseen and difficult examples. Overall, we show that the effective reduction of segmentation failures on outlier cases has a positive impact on not only the average segmentation performance, but also on the estimation of clinical parameters, leading to a better consistency in derived metrics