Shape and texture prior for 2D/3D analysis/ synthesis of neural stem - PowerPoint PPT Presentation

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A French-Singaporean joint laboratory Shape and texture prior for 2D/3D analysis/ synthesis of neural stem cells for fate prediction 2D differentiation and 3D

�� A French-Singaporean joint laboratory Shape and texture prior for 2D/3D analysis/ synthesis of neural stem cells for fate prediction 2D differentiation and 3D reconstruction www.ipal.cnrs.fr
NEUROSPHERE FORMATION ASSAY Embriotic Mouse Brain Neurosphere Growth Differentiation of Progenitor Cell Neuron Astrocyte Progenitor Cell Oligodendrocyte Stem Cell ‣ Culture and purification “ in vitro ” of neural stem cell [1] . ‣ Identify “ proliferation ” and “ multi-potency ”. ‣ No tracks of the origin cell & low survivability. ‣ Three dimension proliferation & two dimension differentiation. [1] S. Ahmed. The culture of neural stem cells. Journal of cellular biochemistery, 2009. ⎋ 2
DATA ‣ Phase contrast time lapse sequence of 2D images. ‣ 40x magnification with 3x3 binning. ‣ 464 x 346 spatial resolution & 20 min time resolution. ‣ Online microscope tracking [2] [2] C.-H. Huang, S. Sankaran, D. Racoceanu, S. Hariharan, and S. Ahmed. Online 3-d tracking of suspension cells imaged with phase-contrast microscopy. IEEE Transactions on Biomedical Engineering, 2012. ⎋ 3
2D-3D REGISTRATION REVIEW ‣ Review: [3] - Dimensionality problematic: projection, back-projection & reconstruction. 2D silhouette 2D silhouettes 2D silhouette back- projection projection 3D reconstruct model 3D model 3D model 3D model [3] P . Markelj, D. Tom ă zev ĭ c, B. Likar, and F. Pern ŭ s. A review of 3d/2d registration methods for image-guided interventions. Medical Image Analysis, 2012. ⎋ 4
PROPOSED METHOD (1) Analysis (3) Registration - Optimisation 2D data stop criterion 2D Cell Detection Registration e g a s m l e I d Convergence e o p Observation projected m loop o data c t s s e o B r c i M 3D Models 3D data Projection optimisation Generation (2) Synthesis ‣ Analysis - cell detection ‣ Synthesis - model generation ‣ Registration / Optimisation - rank & optimise selected model ⎋ 5
CELL DETECTION IN CLUSTER Input Image ROI Hough Partial Level Set Circle Detection Local Maxima Output Detection Detection ⎋ 6
2D IMAGE - 3D MODEL REGISTRATION ‣ Criteria Shape Texture Topology Observation Texture modelisation 1 r (xc,yc) Model -r (xc,yc) r Registration Feature based Intensity based ⎋ 7
PROPOSED METHOD M 1 Projection 1 E 1 M 2 E 2 Projection 2 Observation t E i M i Projection i Score ⎋ 8
RESULTS Raw Temporary Detection results Registration error Model selected ⎋ 9
CELL DETECTION IN CLUSTER VIDEO ⎋ 10
FUTURE WORK ‣ Model generation - Integrate a dynamic generation. New Cell Model Probability ⎋ 11
NEXT STEPS ‣ From animal to human stem cells (skin) ‣ A suite of integrated microscopy systems for imaging anatomies of complex 3D+T (4D) cell culture systems ‣ Drug testing platform for biologists using high-throughput imaging ‣ Next generation of neurodegenerative diseases treatments ⎋ 12
Image & Pervasive Access Lab International joint research unit - UMI CNRS 2955 www.ipal.cnrs.fr ⎋

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