BioMedIA Talk: Enrico Grisan (University of Padova)
2016-09-21 12:00 - 13:00 UTC | Huxley 217/218
Segmenting 2D+T ultrasound imaging data in fetus: improving the estimation of biomarkers of risky adaptations
Abstract: 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.
Bio: 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.

CSI 2016: Workshop & Challenge on Computational Methods and Clinical Applications for Spine Imaging
2016-10-17 | MICCAI 2016, Athens, Greece
Fourth MICCAI Workshop & Challenge on Spine Imaging

In 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:

Clinical applications of spine imaging
Computer-aided diagnosis of spine conditions
Image-guided spine intervention and treatment
Segmentation, registration, detection, and localization of spinal anatomy
Statistical modelling of spinal shape and appearance
Novel imaging and visualization techniques in spine imaging

BACON: Workshop on Brain Analysis using Connectivity Networks
2016-10-17 08:00 - 13:00 | MICCAI’16, InterContinental Athenaeum Athens Syngrou Avenue 89-93, Athens 117 45, Greece
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.
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 17/06/2016!

RAMBO: Workshop on Reconstruction and Analysis of Moving Body Organs
2016-10-17 08:00 - 13:00 | MICCAI’16, InterContinental Athenaeum Athens Syngrou Avenue 89-93, Athens 117 45, Greece
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!
This 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.

BioMedIA Talk: Olaf Ronneberger (Google DeepMind / Albert-Ludwigs-Universität Freiburg)
2017-01-11 12:00 - 13:00
U-net: Convolutional Networks for Biomedical Image Segmentation
Abstract: 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.
References:
O. Ronneberger, P. Fischer, and T. Brox: U-Net: Convolutional Networks for Biomedical Image Segmentation.
MICCAI 2015, available at http://arxiv.org/abs/1505.04597
Ö. 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

BioMedIA Talk: Matthan Caan (Academic Medical Centre & Spinoza Centre for Neuroimaging, Amsterdam)
2017-02-22 12:00 - 13:00
Quantifying MRI: from reconstruction to application in neuroimaging
Abstract: 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.

BioMedIA Talk: Raphael Sznitman (University of Bern, Switzerland)
2017-03-27 12:00 - 13:00 | Huxley 341, Room 341, Huxley Building, Department of Computing, Imperial College London, 180 Queen's Gate, London, London, SW7 2AZ, United Kingdom
Finding the needle in the haystack: detection, tracking and registration in biomedical imaging

Abstract

From 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.

In 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.

Short Bio:
Raphael 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.

BioMedIA Talk: Wenzhe Shi (Twitter)
2017-03-28 11:30 - 12:30 | Huxley 218
Neural network’s for super resolution

Abstract
The 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.

BioMedIA Talk: Loic Le Folgoc (Microsoft Research)
2017-08-29 12:00 - 13:00
Title: Learning structure in complex data: machine learning for medical image segmentation, registration and shape analysis

BioMedIA Talk: Polina Golland (MIT CSAIL)
2017-09-28 11:00 - 12:00
Title: MRI of placenta: from pixels to function
