African Water: Supporting African involvement in the EU Framework Programme.

Water researchers in developing countries have yet to take full advantage of the funding and collaborative research opportunities presented by the EU Framework Programme. There are a variety of reasons for this, such as insufficient information and a lack of previous experience. The African Water initiative aims to increase the involvement of African water researchers through a range of activities including communication and dissemination, capacity building and development, and complementary initiatives. The project has demonstrated that there is a demand for such sector-specific support activities. However, African Water is a small component of a much larger process of partnership between the developed and the less-developed countries of the world, involving many different European and African organisations working across political, institutional and technical domains, and complementing the wide range of actions already being undertaken

Irradiation induced elongation of Fe nanoparticles embedded in silica films

© 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Irradiation with swift heavy ions causes the deformation of Ferric nanoparticles in direction of the ion beam. Fe nanoparticles with mean diameter of about 20 nm were prepared by gas flow sputtering and subsequently confined within silica films. Two silica films wherein two different densities of Fe nanoparticles are encapsulated were irradiated with 50 MeV Ag ions with fluences of few 1014 ions.cm−2 at 300 K and normal incidence. Transmission electron microscopy analysis shows that the spherical Fe nanoparticles are deformed into prolate nanorods aligned in direction of the incident ion beam. The depth distribution profiles of irradiated particles reveal the presence of a critical fluence above which the elongation kinetics becomes dependent on the nanoparticles density. Analysis indicates that for the lower density particles, a saturation length is reached under irradiation to fluence between 3–4 × 1014 ions.cm−2. However, for the higher density, collective growth into aligned nanowires is presumed to take place. Hysteresis curves of the saturation magnetization and coercivity indicate an increasing magnetic anisotropy, which can be correlated with the deformation of nanoparticles in the direction of the ion beam

Ensemble CNN Networks for GBM Tumors Segmentation Using Multi-parametric MRI

Glioblastomas are the most aggressive fast-growing primary brain cancer which originate in the glial cells of the brain. Accurate identification of the malignant brain tumor and its sub-regions is still one of the most challenging problems in medical image segmentation. The Brain Tumor Segmentation Challenge (BraTS) has been a popular benchmark for automatic brain glioblastomas segmentation algorithms since its initiation. In this year, BraTS 2021 challenge provides the largest multi-parametric (mpMRI) dataset of 2,000 pre-operative patients. In this paper, we propose a new aggregation of two deep learning frameworks namely, DeepSeg and nnU-Net for automatic glioblastoma recognition in pre-operative mpMRI. Our ensemble method obtains Dice similarity scores of 92.00, 87.33, and 84.10 and Hausdorff Distances of 3.81, 8.91, and 16.02 for the enhancing tumor, tumor core, and whole tumor regions, respectively, on the BraTS 2021 validation set, ranking us among the top ten teams. These experimental findings provide evidence that it can be readily applied clinically and thereby aiding in the brain cancer prognosis, therapy planning, and therapy response monitoring. A docker image for reproducing our segmentation results is available online at https://hub.docker.com/r/razeineldin/deepseg21

Self-supervised iRegNet for the Registration of Longitudinal Brain MRI of Diffuse Glioma Patients

Reliable and accurate registration of patient-specific brain magnetic resonance imaging (MRI) scans containing pathologies is challenging due to tissue appearance changes. This paper describes our contribution to the Registration of the longitudinal brain MRI task of the Brain Tumor Sequence Registration Challenge 2022 (BraTS-Reg 2022). We developed an enhanced unsupervised learning-based method that extends the iRegNet. In particular, incorporating an unsupervised learning-based paradigm as well as several minor modifications to the network pipeline, allows the enhanced iRegNet method to achieve respectable results. Experimental findings show that the enhanced self-supervised model is able to improve the initial mean median registration absolute error (MAE) from 8.20 (7.62) mm to the lowest value of 3.51 (3.50) for the training set while achieving an MAE of 2.93 (1.63) mm for the validation set. Additional qualitative validation of this study was conducted through overlaying pre-post MRI pairs before and after the de-formable registration. The proposed method scored 5th place during the testing phase of the MICCAI BraTS-Reg 2022 challenge. The docker image to reproduce our BraTS-Reg submission results will be publicly available.Comment: Accepted in the MICCAI BraTS-Reg 2022 Challenge (as part of the BrainLes workshop proceedings distributed by Springer LNCS

IoT and Neural Network-Based Water Pumping Control System For Smart Irrigation

This article aims at saving the wasted water in the process of irrigation using the Internet of Things (IoT) based on a set of sensors and Multi-Layer Perceptron (MLP) neural network. The developed system handles the sensor data using the Arduino board to control the water pump automatically. The sensors measure the environmental factors; namely temperature, humidity, and soil moisture to estimate the required time for the operation of water irrigation. The water pump control system consists of software and hardware tools such as Arduino Remote XY interface and electronic sensors in the framework of IoT technology. The machine learning algorithm such as the MLP neural network plays an important role to support the decision of automatic control of IoT-based irrigation system, managing the water consumption effectively.Comment: 6 pages, 5 figures, 1 tabl

Slicer-DeepSeg: Open-Source Deep Learning Toolkit for Brain Tumour Segmentation

Purpose Computerized medical imaging processing assists neurosurgeons to localize tumours precisely. It plays a key role in recent image-guided neurosurgery. Hence, we developed a new open-source toolkit, namely Slicer-DeepSeg, for efficient and automatic brain tumour segmentation based on deep learning methodologies for aiding clinical brain research. Methods Our developed toolkit consists of three main components. First, Slicer-DeepSeg extends the 3D Slicer application and thus provides support for multiple data input/ output data formats and 3D visualization libraries. Second, Slicer core modules offer powerful image processing and analysis utilities. Third, the Slicer-DeepSeg extension provides a customized GUI for brain tumour segmentation using deep learning-based methods. Results The developed Slicer-DeepSeg was validated using a public dataset of high-grade glioma patients. The results showed that our proposed platform’s performance considerably outperforms other 3D Slicer cloud-based approaches. Conclusions Developed Slicer-DeepSeg allows the development of novel AI-assisted medical applications in neurosurgery. Moreover, it can enhance the outcomes of computer-aided diagnosis of brain tumours. Open-source Slicer-DeepSeg is available at github.com/razeineldin/Slicer-DeepSeg

DeepSeg: Deep Neural Network Framework for Automatic Brain Tumor Segmentation using Magnetic Resonance FLAIR Images

Purpose: Gliomas are the most common and aggressive type of brain tumors due to their infiltrative nature and rapid progression. The process of distinguishing tumor boundaries from healthy cells is still a challenging task in the clinical routine. Fluid-Attenuated Inversion Recovery (FLAIR) MRI modality can provide the physician with information about tumor infiltration. Therefore, this paper proposes a new generic deep learning architecture; namely DeepSeg for fully automated detection and segmentation of the brain lesion using FLAIR MRI data. Methods: The developed DeepSeg is a modular decoupling framework. It consists of two connected core parts based on an encoding and decoding relationship. The encoder part is a convolutional neural network (CNN) responsible for spatial information extraction. The resulting semantic map is inserted into the decoder part to get the full resolution probability map. Based on modified U-Net architecture, different CNN models such as Residual Neural Network (ResNet), Dense Convolutional Network (DenseNet), and NASNet have been utilized in this study. Results: The proposed deep learning architectures have been successfully tested and evaluated on-line based on MRI datasets of Brain Tumor Segmentation (BraTS 2019) challenge, including s336 cases as training data and 125 cases for validation data. The dice and Hausdorff distance scores of obtained segmentation results are about 0.81 to 0.84 and 9.8 to 19.7 correspondingly. Conclusion: This study showed successful feasibility and comparative performance of applying different deep learning models in a new DeepSeg framework for automated brain tumor segmentation in FLAIR MR images. The proposed DeepSeg is open-source and freely available at https://github.com/razeineldin/DeepSeg/.Comment: Accepted to International Journal of Computer Assisted Radiology and Surger

Deep automatic segmentation of brain tumours in interventional ultrasound data

Intraoperative imaging can assist neurosurgeons to define brain tumours and other surrounding brain structures. Interventional ultrasound (iUS) is a convenient modality with fast scan times. However, iUS data may suffer from noise and artefacts which limit their interpretation during brain surgery. In this work, we use two deep learning networks, namely UNet and TransUNet, to make automatic and accurate segmentation of the brain tumour in iUS data. Experiments were conducted on a dataset of 27iUS volumes. The outcomes show that using a transformer with UNet is advantageous providing an efficient segmentation modelling long-range dependencies between each iUS image. In particular, the enhanced TransUNet was able to predict cavity segmentation in iUS data with an inference rate of more than 125 FPS.These promising results suggest that deep learning networks can be successfully deployed to assist neurosurgeons in the operating room

Fifteen years of sector-wide approach (SWAp) in Bangladesh health sector: an assessment of progress

The Ministry of Health and Family Welfare (MOHFW) of the Government of Bangladesh embarked on a sector-wide approach (SWAp) modality for the health, nutrition and population (HNP) sector in 1998. This programmatic shift initiated a different set of planning disciplines and practices along with institutional changes in the MOHFW. Over the years, the SWAp modality has evolved in Bangladesh as the MOHFW has learnt from its implementation and refined the program design. This article explores the progress made, both in terms of achievement of health outcomes and systems strengthening results, since the implementation of the SWAp for Bangladesh’s health sector. Secondary analyses of survey data from 1993 to 2011 as well as a literature review of published and grey literature on health SWAp in Bangladesh was conducted for this assessment. Results of the assessment indicate that the MOHFW made substantial progress in health outcomes and health systems strengthening. SWAps facilitated the alignment of funding and technical support around national priorities, and improved the government’s role in program design as well as in implementation and development partner coordination. Notable systemic improvements have taken place in the country systems with regards to monitoring and evaluation, procurement and service provision, which have improved functionality of health facilities to provide essential care. Implementation of the SWAp has, therefore, contributed to an accelerated improvement in key health outcomes in Bangladesh over the last 15 years. The health SWAp in Bangladesh offers an example of a successful adaptation of such an approach in a complex administrative structure. Based on the lessons learned from SWAp implementation in Bangladesh, the MOHFW needs to play a stronger stewardship and regulatory role to reap the full benefits of a SWAp in its subsequent programming