BioMedIA

BioMedIA Talk: Purang Abolmaesumi (University of British Columbia)
2016-07-06 12:00 - 13:00 UTC
Quantitative Ultrasound Imaging for Diagnosis and Intervention: A Machine Learning Approach
Abstract: 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.
In this talk, I present three main developments in our laboratory based on quantitative ultrasound:

For 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.
For 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.
For 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.

Bio: 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.

BioMedIA Talk: Archontis Giannakidis (Royal Brompton)
2016-05-25 12:00 - 13:00 | Huxley 144
Improving clinical application of cardiac diffusion tensor MRI
Abstract Magnetic resonance diffusion tensor imaging (MRDTI), also known as DTI, has emerged as a powerful non-invasive tool for mapping the orientation-dependent microanatomical organization of fibrous organs such as brain white matter and myocardium. To do so, it elegantly relates the self-diffusion of water molecules that undergo Brownian motion to proton spin relaxation MR signals. In this talk it will be shown how DTI can shed some light on the left-ventricular micro-structural remodeling following hypertensive disease. A framework for the voxelwise registration-based characterization of cardiac disease will be considered. Population and longitudinal studies may benefit from such a scheme. Results from a comparison study will be presented juxtaposing the performance of three tensor distance functions. The selection of a tensor distance function resides in the foundation of the tensor-variate framework, critically affecting many operations in the six-dimensional space of diffusion tensors. This seminar will end by discussing how sparsifying transforms can be used in conjunction with compressive sensing reconstruction to shorten acquisition times.

BioMedIA Talk: Leo Grady (HeartFlow)
2016-05-24 12:00 - 13:00 | Huxley 144
Personalized Blood Flow Simulation from an Image-Derived Model: Changing the Paradigm for Cardiovascular Diagnostics
Abstract: Coronary heart disease is the leading cause of mortality worldwide, accounting for 1/3 of all global deaths. Treatment of stable coronary heart disease is typically performed by medication/lifestyle for a lower disease burden or PCI (stenting) for a greater disease burden. The choice between these treatments is best determined by an invasive diagnostic test that measures blood flow through a diseased area. Unfortunately, this invasive diagnostic test is expensive, dangerous and usually finds a lower disease burden. We are working to change the diagnostics paradigm with blood flow simulation in a personalized heart model that is derived from cardiac CT angiography images. This simulation-based diagnostic is the first clinically available diagnostic that utilizes personalized simulation and is much safer and more comfortable for the patient as well as less expensive. Our diagnostic depends on a hyperaccurate image segmentation of the coronary arteries, physiological modeling and accurate computational fluid dynamics. In this talk I will discuss the algorithms that drive this technology, the machine learning that we’re doing with our database of segmented images and personalized hemodynamics, and the successful clinical trials that have proven the diagnostic accuracy and benefit to patients.

Bio: Leo Grady is the Vice President of Research and Development at HeartFlow since 2012. Prior to joining HeartFlow, he worked at Siemens Corporate Research for nine years as a Principal Research Scientist following his PhD at Boston University. His work has focused on a range of computer vision and medical imaging applications in image segmentation and machine learning. He has written two books on computer vision and data analysis using graph theory, is an editor of several journals in computer vision and was recently inducted as a Fellow of the American Institute of Medical and Biomedical Engineers.

BioMedIA Talk: Ioannis Katramados (COSMONiO)
2016-05-19 16:00 - 17:00 | Huxley 144
Title: The challenges of developing an active deep-learning platform
Abstract:
Deep learning is usually linked to Big Data. However, there are several scientific, engineering and medical imaging problems with limited data available (e.g. rare medical conditions). Can Deep Learning prove a useful tool in such cases? COSMONiO is designing NOUS, an active deep learning platform that aims to make high-accuracy predictions using significantly smaller training datasets. NOUS aims to allow experts from any field to train neural networks without any prior experience. We will discuss the main challenges and how we address them.

BioMedIA Talk: Enzo Ferrante
2016-05-18 12:00 - 13:00 | Huxley 144
Title: Graph-based deformable registration: slice-to-volume mapping and context specific methods
Abstract: Image registration methods, which aim at aligning two or more images into one coordinate system, are among the oldest and most widely used algorithms in computer vision. A particular type of registration algorithm, known as graph-based deformable registration, has become popular during the last decade given its robustness, scalability, efficiency and theoretical simplicity. In this thesis, we extend this flexible framework to new scenarios, and propose novel methodological contributions.
We start by formulating, within the graph-based deformable registration framework, the challenging slice-to-volume registration problem. We introduce a scalable, modular and flexible formulation accommodating low-rank and high order terms, which simultaneously selects the plane and estimates the in-plane deformation through a single shot optimization approach. The other two contributions included in this thesis are related to how semantic information can be encompassed within the registration process. Currently, most of the methods rely on a single metric function explaining the similarity between the source and target images. We argue that incorporating semantic information to guide the registration process will further improve the accuracy of the results, particularly in the presence of semantic labels making the registration a domain specific problem.

BioMedIA talk: William M. Wells
2015-12-16 11:00 - 12:00 | Huxley 218
Sandy Wells is giving a talk on Wednesday 16 Dec, 11:00, in Huxley 218. He will be talking about his registration work and will give an overview of projects in image-guided therapy in his lab.

BioMedIA talk: Eugenio Iglesias
2015-11-13 14:00 - 15:00 | Huxley 342
Building brain atlases and segmentation tools with ex vivo MRI and histology: application to hippocampus, amygdala and beyond
Abstract: Ex vivo modalities make it possible to obtain manual delineations of brain anatomy with very high precision. These delineations can be used to build accurate atlases at very high resolution, which can in turn be applied to the segmentation of brain substructures in in vivo MRI scans. While analyzing the brain at the substructure level holds promise to increase our understanding of such a complex organ (both in health and in disease), it also poses a number of modeling challenges. In this talk, I will first describe the construction of a probabilistic atlas of the hippocampal subfields using a combination of in vivo and ex vivo MRI. Then, I will present some results on in vivo MRI segmented with the atlas. Next, I will present an extension of the atlas to the amygdaloid nuclei. And finally, I will discuss the extension of the framework to the thalamus and other brain structures using histology – a modality that yields excellent contrast and resolution, but also introduces additional difficulties.
Bio: Juan Eugenio Iglesias holds an MSc in Telecom Engineering from the University of Seville (Spain), an MSc in Electrical Engineering from the Royal Institute of Technology (Stockholm, Sweden), and a PhD in Biomedical Engineering from the University of California, Los Angeles (USA). He has also been a postdoctoral researcher at the Martinos Center for Biomedical Imaging (Massachusetts General Hospital / Harvard Medical School), and is currently a Marie Curie postdoctoral fellow at the Basque Center on Cognition, Brain and Language (San Sebastian, Spain). His main research interest is exploring generative models for brain MRI segmentation.

BioMedIA talk: Francois Rousseau
2015-11-10 16:00 - 17:00 | Huxley 503
On early brain development from MRI data
Abstract: The development of ultrafast 2D acquisition sequences has led to significant improvements in the clinical utility of fetal MRI. However, the slice acquisition time is still very critical and has to be as short as possible to reduce the impact of fetal and maternal motion on the exam. Since 2005 there has been a series of techniques coming from computer vision that have been developed to address these limitations in fetal MRI. These motion-correction methods provide a new window into early human brain growth study. I will present in this talk an overview of the current research into fetal brain MRI processing for early brain development studies. I will focus on temporal analysis, and more particularly on cortical folding.
