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X-WR-CALNAME:BioMedIA
X-ORIGINAL-URL:https://biomedia.doc.ic.ac.uk
X-WR-CALDESC:Events for BioMedIA
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DTSTART:20150101T000000
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BEGIN:VEVENT
DTSTART;TZID=UTC:20160706T120000
DTEND;TZID=UTC:20160706T130000
DTSTAMP:20260405T051034
CREATED:20160701T135830Z
LAST-MODIFIED:20160701T135930Z
UID:2829-1467806400-1467810000@biomedia.doc.ic.ac.uk
SUMMARY:BioMedIA Talk: Purang Abolmaesumi (University of British Columbia)
DESCRIPTION:Quantitative Ultrasound Imaging for Diagnosis and Intervention: A Machine Learning Approach \nAbstract: In recent years\, quantitative ultrasound has emerged as a promising technology to improve diagnosis\, increase the precision of interventions\, and automate the acquisition of data specially in point-of- care settings. Advancements in machine learning and computation power have contributed significantly to these developments in quantitative imaging.\nIn this talk\, I present three main developments in our laboratory based on quantitative ultrasound: \n\nFor prostate cancer diagnosis\, I present our work based on the analysis of temporal ultrasound data. I compare the cancer detection and grading accuracy based on temporal ultrasound with multi-parametric MRI in a fusion biopsy study on more than 150 patients. Our results demonstrate that a combination of temporal ultrasound and MRI can accurately predict prostate histopathology determined by biopsy samples.\nFor spine anesthesia and analgesia\, I present the result of our work on combining anatomical models with ultrasound data to facilitate image interpretation in the spine region. Furthermore\, I demonstrate that using advanced machine learning techniques\, automation of image interpretation of the spine is feasible.\nFor echocardiography\, we have access to more than 200\,000 patient records based on clinical data obtained over the last 10 years in Vancouver. I present the initial results of our research on 7\,000 patients both in automatic analysis of data to improve measurement accuracy\, and to assign an image quality factor to the data to facilitate image acquisition.\n\nBio: Purang Abolmaesumi is the Canada Research Chair in Biomedical Engineering\, a Killam Research Prize recipient\, and Professor in the Department of Electrical and Computer Engineering at the University of British Columbia\, Vancouver\, BC\, Canada. He is internationally recognized and has received numerous awards for his pioneering developments in ultrasound image processing\, image registration and image-guided interventions. He is the General Chair of the International Conference on Information Processing in Computer Assisted Intervention\, 2014-2017\, and has served on the program committees of the Medical Image Computing and Computing and Computer Assisted Intervention (MICCAI) and International Society for Optics and Photonics (SPIE) Medical Imaging. Dr. Abolmaesumi is an Associate Editor of the IEEE Transactions on Medical Imaging\, and has served as an Associate Editor of the IEEE Transactions on Biomedical Engineering. His techniques for ultrasound segmentation have been adopted to develop a prostate brachytherapy planning interface that is now the standard-of- care in British Columbia and has been used to treat more than 2\,000 patients.
URL:https://biomedia.doc.ic.ac.uk/event/biomedia-talk-purang-abolmaesumi/
CATEGORIES:Talks & Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20160921T120000
DTEND;TZID=UTC:20160921T130000
DTSTAMP:20260405T051034
CREATED:20160920T093450Z
LAST-MODIFIED:20160920T093450Z
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SUMMARY:BioMedIA Talk: Enrico Grisan (University of Padova)
DESCRIPTION:Segmenting 2D+T ultrasound imaging data in fetus: improving the estimation of biomarkers of risky adaptations \nAbstract: Approximately 10% of the pregnancies are complicated by growth restriction and 7% of pregnancies are complicated by gestational diabetes mellitus (GDM). In addition\, the increasing rate of obesity will likely rise the proportion of pregnancies complicated by both Type 2 diabetes mellitus and GDM. Exposure to either dysglycaemia and poor nutrition may have a long-term impact on the developing child’s physiology\, potentially programming for an increased risk of cardiovascular disease in later life… The full abstract is attached. \nBio: Enrico Grisan graduated in Electrical Engineering at the University of Padova in 2001\, and he was then research consultant for the University of Padova and Nidek Technologies. In 2005 he earned the PhD in Bioengineering jointly from University of Padova and City University London. After being intern at Siemens Corporate Research in 2005\, and then post-doc fellow in Padova from 2005 to 2008\, he has been appointed (tenured) Assistant Professor in Bioengineering since 2008 His main research activities involve the automatic analysis of medical images and the discovery of clinical biomarkers from them. He has been working on retinal images for the quantification or retinopathy-related changes\, on confocal endomicroscopy images for virtual histology\, on multispectral MRI for quantifying cortical lesion burden\, on perfusion patterns in contrast-enhanced ultrasound\, and finally on prenatal ultrasound.
URL:https://biomedia.doc.ic.ac.uk/event/biomedia-talk-enrico-grisan-padova/
LOCATION:Huxley 217/218
CATEGORIES:Talks & Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20161017
DTEND;VALUE=DATE:20161018
DTSTAMP:20260405T051034
CREATED:20160920T094157Z
LAST-MODIFIED:20160920T094830Z
UID:2892-1476662400-1476748799@biomedia.doc.ic.ac.uk
SUMMARY:CSI 2016: Workshop & Challenge on Computational Methods and Clinical Applications for Spine Imaging
DESCRIPTION: Fourth MICCAI Workshop & Challenge on Spine Imaging\n  \n  \n\nIn this workshop\, we are inviting researchers to share and exchange their experience and expertise in spinal imaging and method development. Topics of interest include\, but are not limited to: \n\nClinical applications of spine imaging\nComputer-aided diagnosis of spine conditions\nImage-guided spine intervention and treatment\nSegmentation\, registration\, detection\, and localization of spinal anatomy\nStatistical modelling of spinal shape and appearance\nNovel imaging and visualization techniques in spine imaging
URL:https://biomedia.doc.ic.ac.uk/event/csi-2016-workshop/
LOCATION:MICCAI 2016\, Athens\, Greece
CATEGORIES:Workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20161017T080000
DTEND;TZID=UTC:20161017T130000
DTSTAMP:20260405T051034
CREATED:20160428T115827Z
LAST-MODIFIED:20160428T115827Z
UID:2749-1476691200-1476709200@biomedia.doc.ic.ac.uk
SUMMARY:BACON: Workshop on Brain Analysis using Connectivity Networks
DESCRIPTION:Understanding brain connectivity in a network-theoretic context has shown much promise in recent years. This type of analysis identifies brain organisational principles\, bringing a new perspective to neuroscience. At the same time\, large public databases of connectomic data are now available. However\, connectome analysis is still an emerging field and there is a crucial need for robust computational methods to fully unravel its potential. This workshop provides a platform to discuss the development of new analytic techniques; methods for evaluating and validating commonly used approaches; as well as the effects of variations in pre-processing steps. \nThis year we are co-organising the workshop on Brain Analysis using Connectivity Networks at MICCAI’16 in Athens in the morning of Monday 17/10/2016.  Please consider to submit your work until 17/06/2016! \n 
URL:https://biomedia.doc.ic.ac.uk/event/bacon-workshop-on-brain-analysis-using-connectivity-networks/
LOCATION:MICCAI’16\, InterContinental Athenaeum Athens Syngrou Avenue 89-93\, Athens 117 45\, Greece
ORGANIZER;CN="Sarah Parisot":MAILTO:s.parisot@imperial.ac.uk
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20161017T080000
DTEND;TZID=UTC:20161017T130000
DTSTAMP:20260405T051034
CREATED:20160428T120306Z
LAST-MODIFIED:20160428T120306Z
UID:2754-1476691200-1476709200@biomedia.doc.ic.ac.uk
SUMMARY:RAMBO: Workshop on Reconstruction and Analysis of Moving Body Organs
DESCRIPTION:This year we are co-organising the Workshop on Brain Analysis using Connectivity Networks at MICCAI’16 in Athens in the morning of Monday 17/10/2016.  Please consider to submit your work until 10/06/2016! \nThis workshop targets researchers for whom the effects of motion are critical in image analysis or visualisation. By inviting contributions across application areas we aim to bring together ideas from different fields without being confined to a particular methodology. In particular\, the move from model-based to learning-based methods of modelling over recent years has resulted in increased transferability of techniques between domains. RAMBO provides a forum for the dissemination and discussion of novel developments including\, but not limited to\, motion modelling\, image registration\, segmentation\, classification\, image enhancement\, reconstruction\, motion tracking\, and compressed sensing\, for cardiac\, respiratory\, fetal or interventional applications.
URL:https://biomedia.doc.ic.ac.uk/event/rambo-workshop-on-reconstruction-and-analysis-of-moving-body-organs/
LOCATION:MICCAI’16\, InterContinental Athenaeum Athens Syngrou Avenue 89-93\, Athens 117 45\, Greece
ORGANIZER;CN="Bernhard Kainz":MAILTO:b.kainz@imperial.ac.uk
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20170111T120000
DTEND;TZID=UTC:20170111T130000
DTSTAMP:20260405T051034
CREATED:20170104T151023Z
LAST-MODIFIED:20170105T114230Z
UID:3124-1484136000-1484139600@biomedia.doc.ic.ac.uk
SUMMARY:BioMedIA Talk: Olaf Ronneberger (Google DeepMind / Albert-Ludwigs-Universität Freiburg)
DESCRIPTION:U-net: Convolutional Networks for Biomedical Image Segmentation \nAbstract: In this talk I will present our u-net for biomedical image segmentation. The architecture consists of an analysis path and a synthesis path with additional shortcut-connections. The network is trained end-to-end from scratch with a very low number of annotated images per application. The surprisingly simple strategy to train a network with such a low number of samples is a data augmentation with elastic deformations. Furthermore the u-net can segment arbitrarily large images with a seamless tiling strategy. For 3D images we have developed a training strategy to learn a full 3D segmentation from a few annotated slices per volume. This can be used in a semi-automated setup to create a dense segmentation from the sparse annotations on the same volume\, or in a fully-automated setup\, where sparse annotations speed up the training data generation significantly. \nReferences:\nO. Ronneberger\, P. Fischer\, and T. Brox: U-Net: Convolutional Networks for Biomedical Image Segmentation.\nMICCAI 2015\, available at http://arxiv.org/abs/1505.04597\nÖ. Çiçek\, A. Abdulkadir\, S. S. Lienkamp\, T. Brox\, O. Ronneberger: 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. MICCAI 2016\, available at http://arxiv.org/abs/1606.06650
URL:https://biomedia.doc.ic.ac.uk/event/biomedia-talk-olaf-ronneberger-google-deepmind-albert-ludwigs-universitat-freiburg/
CATEGORIES:Talks & Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20170222T120000
DTEND;TZID=UTC:20170222T130000
DTSTAMP:20260405T051034
CREATED:20170212T110255Z
LAST-MODIFIED:20170212T110310Z
UID:3170-1487764800-1487768400@biomedia.doc.ic.ac.uk
SUMMARY:BioMedIA Talk: Matthan Caan (Academic Medical Centre & Spinoza Centre for Neuroimaging\, Amsterdam)
DESCRIPTION:Quantifying MRI: from reconstruction to application in neuroimaging \nAbstract: To understand the origin and development of neurological disorders\, Magnetic Resonance Imaging (MRI) has proven to provide valuable quantitative measures. In this overview presentation\, I will touch upon several topics. Volumetric measures obtained via segmentation find their application in prenatal famine exposure\, HIV and ischemic stroke. We developed a scattering transform that has no learnable parameters for computing convolutional neural networks in small patient studies. Diffusion MRI is a sensitive method for detecting microstructural changes. We assessed the reproducibility of different models in a multi-site context using a complex diffusion phantom. T1\, T2* and Quantitative Susceptibility Mapping (QSM) allow for mapping myelin and iron content at high resolution at 7T. We present a single MRI sequence for obtaining these measures in a time efficient manner. Further acceleration may be achieved by compressed sensing and potentially deep learning.
URL:https://biomedia.doc.ic.ac.uk/event/biomedia-talk-matthan-caan/
CATEGORIES:Talks & Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20170327T120000
DTEND;TZID=UTC:20170327T130000
DTSTAMP:20260405T051034
CREATED:20170324T121949Z
LAST-MODIFIED:20170324T121949Z
UID:3364-1490616000-1490619600@biomedia.doc.ic.ac.uk
SUMMARY:BioMedIA Talk: Raphael Sznitman (University of Bern\, Switzerland)
DESCRIPTION:Finding the needle in the haystack: detection\, tracking and registration in biomedical imaging\n  \nAbstract\n\n\n\nFrom centimeter-sized observations visible in endoscopy to nanometer large intra-cellular structures discernible with Electron Microscopes\, searching and locating objects of interest in images is a central problem in medical image computing. If anything\, the need for efficient object detection techniques has never been higher due to the advent of cheaper and ever more sophisticated imaging devices\, able of amassing unprecedented quantities of data. And while established search paradigms are showing their limits\, faster methods capable of dealing with larger quantities of data are now indispensable. \n\n\n\nIn this talk\, I will present a computational framework for object detection with efficiency at its core. This framework\, a Bayesian formulation of the traditional “twenty questions” game\, considers a sequential strategy for evaluating different parts of the image data in order to locate the object effectively. In this context\, we will see how dynamic programming and information theory can be used to characterize a provably-optimal search strategy that is both simple to compute and greedy in nature. Using these results\, I will then show how this framework can be used to solve traditional object detection and tracking problems\, as well as non-rigid registration of multimodal multi-scale image data\, allowing for more accurate solutions and large problems to be tackled. \n\n\n\nShort Bio:\nRaphael Sznitman received his B.Sc. in cognitive science from the University of British Columbia (Canada) in 2007. Following this\, he studied Computer Science at Johns Hopkins University where he received his M.Sc and PhD in 2011. From 2011 to 2014\, he was a postdoctoral fellow at the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland. Now an Assistant Professor at the ARTORG Center for Biomedical Engineering Research of the University of Bern (Switzerland)\, his research interests lie in the fields of computer vision and machine learning with applications to biomedical imaging\, surgery and histology.
URL:https://biomedia.doc.ic.ac.uk/event/biomedia-talk-raphael-sznitman-university-of-bern-switzerland/
LOCATION:Huxley 341\, Room 341\, Huxley Building\, Department of Computing\, Imperial College London\, 180 Queen's Gate\, London\, London\, SW7 2AZ\, United Kingdom
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20170328T113000
DTEND;TZID=UTC:20170328T123000
DTSTAMP:20260405T051034
CREATED:20170324T121905Z
LAST-MODIFIED:20170324T121905Z
UID:3375-1490700600-1490704200@biomedia.doc.ic.ac.uk
SUMMARY:BioMedIA Talk: Wenzhe Shi (Twitter)
DESCRIPTION:Neural network’s for super resolution\n  \nAbstract\nThe most important parts for using neural network’s for super resolution are data\, network architecture and the objective functions. In this talk we will discuss our innovation in network architecture to make our algorithms running real time on mobile devices as well as how we achieved a new level of perceived qualities for super resolved images by redefining the object functions. Finally we will close the talk by showing what’s the best practice when extending image super resolution techniques to videos.
URL:https://biomedia.doc.ic.ac.uk/event/biomedia-talk-wenzhe-shi-twitter/
LOCATION:Huxley 218
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20170829T120000
DTEND;TZID=UTC:20170829T130000
DTSTAMP:20260405T051034
CREATED:20170819T063022Z
LAST-MODIFIED:20170828T124722Z
UID:3545-1504008000-1504011600@biomedia.doc.ic.ac.uk
SUMMARY:BioMedIA Talk: Loic Le Folgoc (Microsoft Research)
DESCRIPTION:Title: Learning structure in complex data: machine learning for medical image segmentation\, registration and shape analysis
URL:https://biomedia.doc.ic.ac.uk/event/biomedia-talk-loic-le-folgoc-microsoft-research/
CATEGORIES:Talks & Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20170928T110000
DTEND;TZID=UTC:20170928T120000
DTSTAMP:20260405T051034
CREATED:20170926T082146Z
LAST-MODIFIED:20170926T082211Z
UID:3571-1506596400-1506600000@biomedia.doc.ic.ac.uk
SUMMARY:BioMedIA Talk: Polina Golland (MIT CSAIL)
DESCRIPTION:Title: MRI of placenta: from pixels to function
URL:https://biomedia.doc.ic.ac.uk/event/biomedia-talk-polina-golland-mit-csail/
CATEGORIES:Talks & Seminars
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