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HAHN Stéphan


LISA - Image Analysis

LISA (Laboratory of Image Synthesis and Analysis) brings together expertise in image processing and analysis, pattern recognition, image synthesis and virtual reality. Its LISA-IA unit focuses on the fields of image analysis and pattern recognition and develops new methods for 2D and 3D object segmentation, recognition or tracking, multi-modal image registration, as well as machine and deep learning methods for signal and image processing. In the latter context, research is being carried out on the ability to deal with imperfect (weak or noisy) annotations and on methods of evaluating algorithms in such situations where the ground truth is not available. 
Developed algorithms are related to biomedical and industrial applications. Following a problem-centered approach, the unit tackles all hardware and software aspects of the chain in multidisciplinary teams (MDs, biologists, engineers, computer scientists, mathematicians, as well as art historians and archaeologists) over multi-institutional collaborations to deliver functional applications. The research is funded both by institutional/public funds and industry collaborations. LISA's achievements include one patent, several highly cited biomedical papers, implementation of acquisition and thermoregulation devices for live cell imaging, multi-media event organization and international cultural heritage projects.


PICRIB-Platform for Imaging in Clinical Research in Brussels

Partners: Department of Electronics and Informatics (ETRO), VUB;Radiology department (RD), ULB - Hôpital Erasme (HE);Radiology department (RD), UCL-Cliniques Universitaires Saint-Luc (CUSL); Radiology Department (RD), VUB-UZBrussel;Laboratories of Image, Signal processing and Acoustics (LISA),  LB;Department of Translational Research, Radiotherapy and Imaging (TRI), EORTC.
Development of a clinical imaging platform in support of the clinical and research activities involving imaging, in particular for Brussels based academic research groups, the pharma and devices industry and SMEs.


Symbol recognition, and in particular optical character recognition (OCR), can be consider as a mature domain. However when it comes to structured document, in particular schematics or blueprints, the complexity and the specificity of the document is increasing dramatically. The usual approach relies on line identification, symbol recognition and more generally OCR limited to portion of the document. Often ontologies, that are machine-compatible description of concepts, are defined to describe/analyse the document in a structured way. Ontologies are domain specific and require an important expert input.
Blueprint project proposes to implement and train state of the art techniques of machine vision in order to provide the user with an interactive constantly learning system that will reinforce its prediction accuracy by a continuous human interaction. Moreover the chosen approach will enable to apply the same framework to dataset from diverse origin, in order to cope with the versatility of the blueprint digitization problem.