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Short Biography


Arnaud Gelas received a Master degree in Electrical Engineering (2002), Master of Sciences (2003), and a Ph. D. (2006) from National Institute of Applied Sciences in Lyon (INSA de Lyon), France.

During his master degrees, he was a research associate in the CREATIS laboratory and was interested in 3D surface mesh processing and compression for biomedical applications.

During his Ph.D., under the supervision of Pr Remy Prost and Dr Takashi Kanai, Arnaud Gelas worked on applications of Compactly Supported Radial Basis Functions. He spent 6 months in the Research and Innovation division of Dassault Systemes, developing new technologies for surface representation in CAD context; 1 year and half at KEIO University in Japan where he worked, in collaboration with Dr Yutaka Ohtake, on surface reconstruction from sparse samples; 1 year at CREATIS laboratory where he developed parametric level-set formulation for image segmentation. In collaboration with clinicians, he successfully applied this new technique to extract myocardial regions in echocardiography.

In 2007, Arnaud Gelas joined the Biomedical Imaging Laboratory, SBIC, A*STAR, as a research fellow where he worked under the supervision of Pr Wieslaw Novinski. His research focused on the generation and improvement of existing brain atlases. He was also leading a project about the development of a new cortical surface modeling which is now used in a commercial application.

In 2008, Arnaud Gelas joined the Megason Lab, Department of Systems Biology, Harvard Medical School, as Senior R&D Engineer. He has been managing the GoFigure2 project and the Level-Sets refactoring effort as part of the ITKv4 American Recovery and Reinvestment Act. His research focus on biomedical image processing (filtering, cell segmentation, cell tracking...) in confocal 3D+t multichannel images. 

Research Interests

  • Biomedical Image Analysis
    • Cell Segmentation, Tracking, Lineage Reconstruction
    • High Throughput Image Analysis
    • etc.
  • Software development
  • Level-Sets
  • Geometry Processing



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