Below the list of projects cofunded by the María de Maeztu program (selected via internal calls, in this link the first one launched at the beginning of the program, and in this link the second one, launched in September 2016).
In addition, the program supported:
- joint calls for cooperation between DTIC and the UPF Department of Experimental and Health Sciences (CEXS), also recognised as a María de Maeztu Unit of Excellence. Here the link to the second call (November 2017). The first call took place in January 2017.
- its own Open Science and Innovation program
- a pilot program to promote educational research collaborations with industry
The detail of the internal procedures for the distribution of funds associated to the program can be found here
Bio Image and Signal Analysis
Bio Image and Signal Analysis
Bio Image and Signal Analysis
Web of the project for updated details
https://www.upf.edu/en/web/bia
Web of the project for updated details: https://www.upf.edu/en/web/bia
Researchers associated to [email protected] use data- and knowledge-driven (machine learning; image&signal analysis; computational modelling) algorithms to support both the extraction of pertinent information from the current (large dataset) information sources as well as for supporting optimal decision-making for biomedical research as well as patient treatment.
These approaches used allow to do supervised learning to suggest the most efficient approach for data analysis and decision processes to address a specific clinical question, as well as to perform unsupervised analysis of the data, enabling the suggestion of novel information content towards further hypothesis-driven assessment of the data thus supporting new discoveries in biomedical research as well as address clinic questions. Challenges include data dimensionality reduction and data-driven decision-making.
[email protected] is embedded in the BCN MedTech unit, which is a collaboration of the groups: PhySense (Sensing in Physiology and Biomedicine) – O. Camara/B. Bijnens; SIMBiosys (Simulation, Imaging and Modelling for Biomedical Systems) – M.A. González Ballester /G. Piella; NTSA (Nonlinear Time Series Analysis) – R. Andrzejak and BERG (Biomedical Electronics Research Group) – A. Ivorra. Given the strong interdisciplinarity, [email protected] further links to the groups working in Artificial Intelligence; Natural Language Processing; and Multimedia Analysis and Visualisation.
[email protected] aims at providing a comprehensive approach towards Image and Signal Analysis in Biomedical applications. Therefore, the programme encompasses the following complementary aspects (see figure):
- Algorithmic research: Specific, interdisciplinary, research projects will be performed , predominantly in the Bio-Image Analysis field (microscopy and high-resolution medical imaging) and non-linear signal analysis.
- Repository: High resolution imaging and signal datasets will be made available for the wider research community.
- Analysis Tools: Tools developed within BCN MedTech as well as in the framework of [email protected] will be made available for researchers trough a platform for tool-sharing and tool/algorithm annotation.
- Benchmarking: Benchmarking approaches, linking tools and datasets (both [email protected] as well as external ones) will be investigated and implemented.
- Consultancy: Specialised (technical/processing) knowledge, especially on Bio-Image Analysis, will be made accessible for biomedical researchers in the Barcelona Area.
- Training: [email protected] will put a special emphasis on the organisation of training events for a wide range of researchers and specialist. This will range from Bio-Image Analysis tools courses and tagatons for tools description; over student summer schools; going towards specialised (clinical/biomedical) workshops.
- Networking: [email protected] will ensure wide dissemination in the research community trough active participation and organisation of networking events, linked to networking projects (e.g. COST project – NEUBIAS and COSMOS) and international and local organisations.
Some special focus will be on:
- Generalisation of algorithms and automated analyses.
The large diversity applications and imaging/signal acquisition techniques requires the development of algorithms for analysis tasks to perform robustly and equally well under different scenarios. Most of the existing tools or methods have been tailored for specific applications or projects. Applying to other datasets typically requires parameter tuning or re-programming of the software. Manual software adaptations are tedious and raise obstacles for life scientists due to their lack of expertise in software engineering.
Machine learning methods provide an effective way to automate the analysis, as they seek to use intrinsic data structure and expert annotations to infer models that can be used to solve versatile data analysis tasks. By providing a general solution through learning processing rules from examples rather than relying on manual adjustments of parameters or pre-defined processing steps, machine learning methods are particularly more flexible than conventional image processing techniques for solving complex multi-dimensional data analysis tasks.
Furthermore, automation is playing an increasingly important role in image/signal processing and analysis. We expect that the techniques to be developed within [email protected] will be broad enough and can effectively address the parameter-tuning problem. Additionally, for reproducibility and easy access, it should be feasible to integrate them into the automation pipeline from the commonly used platforms in the community.
- Networking through community support.
Every biologist/biomedical researcher now has the potential to investigate the multidimensional operation of biological systems. At the same time, they are struggling with the growing amount of data at hand and the augmenting complexity of image/signal analysis methods. At least to some extent, their ability for customization of image analysis workflows is required but this knowledge is not taught in biology curriculum in universities. Also, the number of tools is increasing, which inevitably adds further hurdles in their daily efforts to find matching tools to address their specific biological questions. Ultimately, automation of image analysis is becoming a necessity as highly-automated acquisition of bioimage data has become more accessible (e.g. high-throughput screening microscopy). For all these reasons, various training and support services should be provided, such as software usage support, training on bioimage analysis, techniques for common analysis topics: visualization, segmentation, colocalization, deconvolution, registration, morphology analysis, etc. Beyond education purposes, we believe that this will promote research projects and collaborations both locally and internationally. In return, these activities could lead to potential interesting research projects and collaboration in the future.
- (Inter-) national collaboration.
To achieve these objectives, [email protected] will also seek external (technical- and application-wise) collaborations with both local institutes (e.g. IRB Barcelona, CRG, IBEC, CEXS) and international ones (e.g. University of Heidelberg, ETH, EMBL). Most of which we have worked or collaborated with previously.
To learn more:
- Presentation of the project at the Data-driven Knowledge Extraction Workshop, June 2016 (Slides)
Principal researchers
Bart Bijnens Ralph Andrzejak Óscar Cámara Miguel Ángel González Ballester Antoni Ivorra Gemma PiellaResearchers
Chong Zhang Carlos Yagüe Anders Jonsson Josep BlatRelated Assets:
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Sanroma G, Benkarim OM, Piella G, Lekadir K, Hahner N, Eixarch E, González Ballester MA. Learning to combine complementary segmentation methods for fetal and 6-month infant brain MRI segmentation. Computerized Medical Imaging and Graphics.
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López-Guimet J, Peña-Pérez L, Bradley RS, García-Canadilla P, Disney C, Geng H, Bodey AJ, Withers PJ, Bijnens B, Sherratt MJ, Egea G. MicroCT imaging reveals differential 3D micro-scale remodelling of the murine aorta in ageing and Marfan syndrome. Theranostics
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Ruiz Wills C, Foata B, González Ballester MA, Karppinen J, Noailly J. Theoretical Explorations Generate New Hypotheses About the Role of the Cartilage Endplate in Early Intervertebral Disk Degeneration. Frontiers in Physiology
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Study finds if muscle tension has impact on stability of the standing posture
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Bioimage Analysis Workshops and Web launched
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WebGLStudio - Web graphics platform to create interactive 3D scenes directly from the browser
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[EEG] The Bern-Barcelona EEG database
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[BIOIMAGE] Vascular structures
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[BIOIMAGE] 2D bright field Fission yeast cell images with ground truth annotations
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Paun B, Bijnens B, Iles T, Iaizzo P, Butakoff C. Patient Independent Representation of the Detailed Cardiac Ventricular Anatomy. Medical Image Analysis
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[BIOIMAGE] 2D phase contrast HeLa cells images with ground truth annotations
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[BIOIMAGE] 2D bright field yeast cell images with ground truth annotations
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Cárdenes R, Zhang C, Klementieva O, Werner S, Guttmann P, Pratsch C, Cladera J, Bijnens B. 3D Membrane Segmentation and Quantification of Intact Thick Cells using Cryo Soft X-ray Transmission Microscopy: A Pilot Study. PLoS ONE 12(4): e0174324
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Wang X, Liu Y, Wu Z, Zhou M, González Ballester MA, Zhang C. Automatic labeling of vascular structures with topological constraints via HMM. MICCAI2017
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Mangado N, Ceresa M, Benav H, Mistrik P, Piella G, González Ballester MA. Towards a Complete In Silico Assessment of the Outcome of Cochlear Implantation Surgery. Mol Neurobiol (2017)
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CfP Patch-based techniques for medical imaging (PatchMI) workshop at MICCAI 2017
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Mangado N., Piella G., Noailly J., Pons-Prats J., González Ballester M.A. Analysis of uncertainty and variability in finite element computational models for biomedical engineering: characterization and propagation. Frontiers in Bioengineering and Biotechnology.
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[BIOIMAGE] 3D Cryo Soft X-ray Transmission Microscopy data of Intact Thick Cells for Membrane Segmentation and Quantification
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Soto-Iglesias D, Duchateau N, Butakoff C, Andreu D, Fernández-Armenta J, Bijnens B, Berruezo A, Sitges M, Cámara. Quantitative Analysis of Electro-Anatomical Maps: Application to an Experimental Model of Left Bundle Branch Block/Cardiac Resynchronization Therapy. IEEE Journal of Translational Engineering in Health and Medicine.
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The 2nd María de Maeztu Biomage Analysis Workshop has been a success!
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Freesurfer MRI course at UPF in September
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Pezzatini D, Yagüe C, Rudenick P, Blat J, Bijnens B, Camara O. A Web-Based Tool for Cardiac Dyssynchrony Assessment on Ultrasound Data. Eurographics Workshop on Visual Computing for Biology and Medicine
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14th European Echocardiography Course - October 9th - 12th at DTIC-UPF
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Gerber N, Reyes M, Barazzetti L, Kjer HM, Vera S, Stauber M, Mistrik P, Ceresa M, Mangado N, Wimmer S, Stark T, Paulsen RR, Weber S, Caversaccio M, González Ballester MA. A multiscale imaging and modelling dataset of the human inner ear. Nature Scientific Data
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Development of the Rocket platform for collaborative clinical studies
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[PhD thesis] Image based analysis and modeling of the detailed cardiac ventricular anatomy
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[IMAGE] A multiscale imaging and modelling dataset of the human inner ear
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Paun B, Bijnens B, Butakoff C. Relationship between the left ventricular size and the amount of trabeculations. International Journal for Numerical Methods in Biomedical Engineering
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V-Heart SN, the Spanish network to facilitate the use of virtual hearts in the daily clinical practice
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Rocket viewer - visualiser for multiple types of data (such as medical or biological data) from the web or the local file system
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Ceresa M, Olivares AL, Noailly J, González Ballester MA. Coupled Immunological and Biomechanical Model of Emphysema Progression. Frontiers in Physiology
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A computational model simulates the clinical evolution of Chronic Obstructive Pulmonary Disease
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Raote I, Ortega-Bellido M, Santos A, Foresti O, Zhang C, Garcia-Parajo MF, Campelo F, Malhotra V. TANGO1 builds a machine for collagen export by recruiting and spatially organizing COPII, tethers and membranes. eLife 2018
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Mangado N, Pons-Prats J, Coma M, Mistrik P, Piella G, Ceresa M, González Ballester MA. Computational evaluation of cochlear implant surgery outcomes accounting for uncertainty and parameter variability. Frontiers in Physiology-Computational Physiology and Medicine
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Toumanidou T, Noailly J, Ceresa M, Zhang C, López-Linares K, Macía I, González Ballester M.A. Patient-specific modeling of unruptured human abdominal aortic aneurysms using deformable hexahedral meshes. International Journal of Computer Assisted Radiology and Surgery
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Funke J, Zhang C, Tobias P, Gonzalez Ballester MA, Saalfeld S. The Candidate Multi-Cut for Cell Segmentation. IEEE International Symposium on Biomedical Imaging (ISBI'18)
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Raote I, Ortega Bellido M, Pirozzi M, Zhang C, Melville d, Parashuraman S, Zimmermann T, Malhotra V. TANGO1 assembles into rings around COPII coats at ER exit sites. Journal of Cell Biology
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2nd Barcelona Virtual Physiological Human Summer School, May 22-26
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2nd NEUBIAS Training Schools and Taggathon on Biomage Analysis
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[MSc thesis] Cross-Entropy method for Kullback-Leibler control in multi-agent systems
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New awards for PhD students at the Department
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Cytosolic and nuclear optical nanosensors, second project selected in the joint DTIC-CEXS call
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Applications of Machine Learning for Medical Technology
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Rocket: a unified platform aimed at assisting clinicians, allowing them to store, visualize and process different types of clinical data
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Wang S., Zhang C., González Ballester M.A., Yarkony J. Efficient pose and cell segmentation using column generation. ArXiv preprint
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Bill and Melinda Gates Foundation support research on machine learning for high-risk pregnancies in Pakistan
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euCanSHAre: A new H2020 project coordinated by DTIC-UPF on the topic big data and data sharing for personalised medicine research in cardiology
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Leveraging Cloud Technology for Healthcare and Biomedicine: Rocket and other Prominent Examples
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Dalmazzo D, Tassani S, Ramírez R. A Machine Learning Approach to Violin Bow Technique Classification: a Comparison Between IMU and MOCAP systems. iWOAR '18 Proceedings of the 5th international Workshop on Sensor-based Activity Recognition and Interaction
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Paun B, Bijnens B, Cook AC, Mohun, TJ, Butakoff C. Quantification of the detailed cardiac left ventricular trabecular morphogenesis in the mouse embryo. Medical Image Analysis
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Researchers of BCN MedTech win a "Special Mention of Excellence" award at the VII Meeting of the National Chapter of the ESB
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An algorithm might indicate which patients can benefit from cardiac resynchronization therapy
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Torrents-Barrena J, Piella G, Masoller N, Gratacós E, Eixarch E, Ceresa M, González Ballester MA. Segmentation and classification in MRI and US fetal imaging: Recent trends and future prospects. Medical Image Analysis
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Garcia-Canadilla P, Dejea H, Bonnin A, Balicevic V, Loncaric S, Zhang C, Butakoff C, Aguado-Sierra J, Vázquez M, Jackson L H, Stuckey D J, Rau C, Stampanoni M, Bijnens B, Cook. Complex Congenital Heart Disease Associated With Disordered Myocardial Architecture in a Midtrimester Human Fetus. Circulation: Cardiovascular Imaging.
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Cikes, M. , Sanchez‐Martinez, S. , Claggett, B. , Duchateau, N. , Piella, G. , Butakoff, C. , Pouleur, A. C., Knappe, D. , Biering‐Sørensen, T. , Kutyifa, V. , Moss, A. , Stein, K. , Solomon, S. D., Bijnens, B. Machine learning‐based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy. European Journal of Heart Failure
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Preoperative Planning and Simulation Framework for Twin-to-Twin Transfusion Syndrome Fetal Surgery
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Call for papers for the ISBI/IEEE International Symposium on Biomedical Imaging
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euCanShare, towards a centralised, secure and sustainable platform for enhanced cross-border data sharing in cardiology
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Videos from the HUMAINT workshop on the impact of Artificial Intelligence in healthcare available
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Garcia-Canadilla P, de Vries T, Gonzalez-Tendero A, Bonnin A, Gratacos E, Crispi F, Bijnens B, Zhang C. Structural coronary artery remodelling in the rabbit fetus as a result of intrauterine growth restriction. PLoS ONE
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[BIOIMAGE] CRT-EPiggy19 challenge
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Juhl KA, Paulsen RR, Dahl AB, Dahl VA, De Backer O, Kofoed K, Camara O. Guiding 3D U-nets with signed distance fields for creating 3D models from images. Medical Imaging with Deep Learning (MIDL2019)
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2nd Fetal Cardiac Function Symposium – October 8th at DTIC-UPF
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Torrents-Barrena J, López-Velazco R, Piella G, Masoller N, Valenzuela-Alcaraz B, Gratacós E, Eixarch E, Ceresa M, González Ballester MA. TTTS-GPS: Patient-specific preoperative planning and simulation platform for twin-to-twin transfusion syndrome fetal surgery. Computer Methods and Programs in Biomedicine.
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Torrents-Barrena J, Piella G, Masoller N, Gratacós E, Eixarch E, Ceresa M, González Ballester MA. Fully automatic 3D reconstruction of the placenta and its peripheral vasculature in intrauterine fetal MRI. Medical Image Analysis.
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Call For Papers: 8th Clinical Image-based Procedures Workshop (CLIP 2019)
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Cardiac Resynchronization Therapy ElectroPhysiological challenge 2019 CRT-EPiggy19 challenge
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Dejea H, Garcia-Canadilla P, Cook AC, Guasch E, Zamora M, Crispi F, Stampanoni M, Bijnens B, Bonnin A . Comprehensive Analysis of Animal Models of Cardiovascular Disease using Multiscale X-Ray Phase Contrast Tomography. Nature Scientific Reports.
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[PhD thesis] Towards the improvement of decision tree learning: a perspective on search and evaluation
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Hospitals Sant Joan de Déu and Clínic together with UPF create a worldwide pioneering surgical navigation system for fetal surgery
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[PhD thesis] Computational anatomy as a driver of understanding structural and functional cardiac remodeling
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[PhD thesis] Machine learning to support exploring and exploiting real-world clinical longitudinal data
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[PhD thesis] Nonlinear signal analysis of micro and macro electroencephalographic recordings from epilepsy patients
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Aguado AM, Olivares AL, Yagüe C, Silva E, Nuñez-García M, Fernandez-Quilez A, Mill J, Genua I, Arzamendi D, De Potter T, Freixa X, Camara O. In silico Optimization of Left Atrial Appendage Occluder Implantation Using Interactive and Modeling Tools. Frontiers in Physiology.