The Experts below are selected from a list of 167739 Experts worldwide ranked by ideXlab platform
A M Abbosh - One of the best experts on this subject based on the ideXlab platform.
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software defined radar for Medical Imaging
IEEE Transactions on Microwave Theory and Techniques, 2016Co-Authors: J Marimuthu, Konstanty Bialkowski, A M AbboshAbstract:A low-cost reconfigurable microwave transceiver using software-defined radar is proposed for Medical Imaging. The device, which uses generic software-defined radio (SDR) technology, paves the way to replace the costly and bulky vector network analyzer currently used in the research of microwave-based Medical Imaging systems. In this paper, calibration techniques are presented to enable the use of SDR technology in a bioMedical Imaging system. With the aid of an RF circulator, a virtual 1-GHz-wide pulse is generated by coherently adding multiple frequency spectrums together. To verify the proposed system for Medical Imaging, experiments are conducted using a circular scanning system and directional antenna. The system successfully detects small targets embedded in a liquid emulating the average properties of different human tissues.
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stepped frequency continuous wave software defined radar for Medical Imaging
IEEE Antennas and Propagation Society International Symposium, 2014Co-Authors: J Marimuthu, Konstanty Bialkowski, A M AbboshAbstract:Software defined radar (SDRadar) is investigated as a possible transceiver for microwave-based Medical Imaging applications. In radar-based microwave Imaging, wideband pulses are synthetically created using a stepped frequency continuous wave (SFCW) varying over the entire wideband frequency range. Although the resolution is typically low compared with other tools, such as X-ray, microwave Imaging is gaining popularity due to low health risk, and low-cost compared to conventional Medical Imaging systems. Recent works show the feasibility of this technique by employing commercial vector network analysers (VNA); however, using VNA results in a rigid, bulky and expensive system. To realize a mass screening diagnostic tool, a low-cost portable system based on SDRadar as a transceiver is proposed. The SFCW-SDRadar prototype is implemented using both open source software and hardware. The software part of the radar is realized using GNU Radio, whilst the hardware part is implemented using bladeRF.
Daniil I. Nikitichev - One of the best experts on this subject based on the ideXlab platform.
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From Medical Imaging data to 3D printed anatomical models
PLOS ONE, 2017Co-Authors: Thore M. Bücking, Emma R. Hill, James Robertson, Efthymios Maneas, Andrew A. Plumb, Daniil I. NikitichevAbstract:Anatomical models are important training and teaching tools in the clinical environment and are routinely used in Medical Imaging research. Advances in segmentation algorithms and increased availability of three-dimensional (3D) printers have made it possible to create cost-efficient patient-specific models without expert knowledge. We introduce a general workflow that can be used to convert volumetric Medical Imaging data (as generated by Computer Tomography (CT)) to 3D printed physical models. This process is broken up into three steps: image segmentation, mesh refinement and 3D printing. To lower the barrier to entry and provide the best options when aiming to 3D print an anatomical model from Medical images, we provide an overview of relevant free and open-source image segmentation tools as well as 3D printing technologies. We demonstrate the utility of this streamlined workflow by creating models of ribs, liver, and lung using a Fused Deposition Modelling 3D printer.
Ricky K Taira - One of the best experts on this subject based on the ideXlab platform.
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Medical Imaging informatics
Advances in Experimental Medicine and Biology, 2010Co-Authors: Ricky K TairaAbstract:Medical Imaging Informatics provides an overview of this growing discipline, which stems from an intersection of bioMedical informatics, Medical Imaging, computer science and medicine. Supporting two complementary views, this volume explores the fundamental technologies and algorithms that comprise this field, as well as the application of Medical Imaging informatics to subsequently improve healthcare research. Clearly written in a four part structure, this introduction follows natural healthcare processes, illustrating the roles of data collection and standardization, context extraction and modeling, and Medical decision making tools and applications. Medical Imaging Informatics identifies core concepts within the field, explores research challenges that drive development, and includes current state-of-the-art methods and strategies.
J Marimuthu - One of the best experts on this subject based on the ideXlab platform.
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software defined radar for Medical Imaging
IEEE Transactions on Microwave Theory and Techniques, 2016Co-Authors: J Marimuthu, Konstanty Bialkowski, A M AbboshAbstract:A low-cost reconfigurable microwave transceiver using software-defined radar is proposed for Medical Imaging. The device, which uses generic software-defined radio (SDR) technology, paves the way to replace the costly and bulky vector network analyzer currently used in the research of microwave-based Medical Imaging systems. In this paper, calibration techniques are presented to enable the use of SDR technology in a bioMedical Imaging system. With the aid of an RF circulator, a virtual 1-GHz-wide pulse is generated by coherently adding multiple frequency spectrums together. To verify the proposed system for Medical Imaging, experiments are conducted using a circular scanning system and directional antenna. The system successfully detects small targets embedded in a liquid emulating the average properties of different human tissues.
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stepped frequency continuous wave software defined radar for Medical Imaging
IEEE Antennas and Propagation Society International Symposium, 2014Co-Authors: J Marimuthu, Konstanty Bialkowski, A M AbboshAbstract:Software defined radar (SDRadar) is investigated as a possible transceiver for microwave-based Medical Imaging applications. In radar-based microwave Imaging, wideband pulses are synthetically created using a stepped frequency continuous wave (SFCW) varying over the entire wideband frequency range. Although the resolution is typically low compared with other tools, such as X-ray, microwave Imaging is gaining popularity due to low health risk, and low-cost compared to conventional Medical Imaging systems. Recent works show the feasibility of this technique by employing commercial vector network analysers (VNA); however, using VNA results in a rigid, bulky and expensive system. To realize a mass screening diagnostic tool, a low-cost portable system based on SDRadar as a transceiver is proposed. The SFCW-SDRadar prototype is implemented using both open source software and hardware. The software part of the radar is realized using GNU Radio, whilst the hardware part is implemented using bladeRF.
Julien Cohenadad - One of the best experts on this subject based on the ideXlab platform.
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unsupervised domain adaptation for Medical Imaging segmentation with self ensembling
NeuroImage, 2019Co-Authors: Christian S Perone, Pedro Ballester, Rodrigo C Barros, Julien CohenadadAbstract:Abstract Recent advances in deep learning methods have redefined the state-of-the-art for many Medical Imaging applications, surpassing previous approaches and sometimes even competing with human judgment in several tasks. Those models, however, when trained to reduce the empirical risk on a single domain, fail to generalize when applied to other domains, a very common scenario in Medical Imaging due to the variability of images and anatomical structures, even across the same Imaging modality. In this work, we extend the method of unsupervised domain adaptation using self-ensembling for the semantic segmentation task and explore multiple facets of the method on a small and realistic publicly-available magnetic resonance (MRI) dataset. Through an extensive evaluation, we show that self-ensembling can indeed improve the generalization of the models even when using a small amount of unlabeled data.
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unsupervised domain adaptation for Medical Imaging segmentation with self ensembling
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Christian S Perone, Pedro Ballester, Rodrigo C Barros, Julien CohenadadAbstract:Recent advances in deep learning methods have come to define the state-of-the-art for many Medical Imaging applications, surpassing even human judgment in several tasks. Those models, however, when trained to reduce the empirical risk on a single domain, fail to generalize when applied to other domains, a very common scenario in Medical Imaging due to the variability of images and anatomical structures, even across the same Imaging modality. In this work, we extend the method of unsupervised domain adaptation using self-ensembling for the semantic segmentation task and explore multiple facets of the method on a small and realistic publicly-available magnetic resonance (MRI) dataset. Through an extensive evaluation, we show that self-ensembling can indeed improve the generalization of the models even when using a small amount of unlabelled data.
