Algorithms for matching of multimodal and inter-individual datasets: Functional and structural analysis of rodent brains and for the analysis of dynamics of neuronal processes

Collaborators:

• Prof. Dr. Eckart D. Gundelfinger
• Werner Zuschratter (joint IfN project)

The correlation of anatomical and morphological brain data with such obtained from functional studies is essential for the appreciation of structure-function relationships in the brain. Matching a group of individual datasets or data collected from the same animal using different methods to a reference template (a virtual brain) requires geometric transformation techniques (warping) to reduce structural variations. We have develop a new point-based distance-weighted warping method which optimizes the local weighting factors of displacement vectors (Optimized warping). To perform the transformation between the datasets landmarks have to be defined. In large three-dimensional datasets manual setting of sufficient landmarks is nearly impracticable. Consequently we developed fast automatic procedures for landmark definition which inherently have a spatial correspondance between pairs of landmarks (see figure). The application of these warping techniques to CLSM datasets showing dynamics of neuronal processes results in deformation fields, which is time-dependent but therefore quantifies these cellular dynamics.

Figure: Surface view of a brain image dataset together with 1174 automatically defind landmarks. The outer surface of the brain and four different structures within the brain (septum (SE), striatum (ST), thalamus (T), hippocampal formation (H)) were segmented manually and then surface rendered by the visualization software AMIRA.

More Details: http://amira.zib.de/

References:

Pielot R, Scholz M, Obermayer K, Scheich H, Gundelfinger ED and Hess A (2003). A new point-based warping method for enhanced and simplified analysis of functional brain image data. Neuroimage 19, 1716-1729. PubMed Fulltext

Pielot R, Scholz M, Obermayer K, Gundelfinger ED and Hess A (2002). Comparison of different 3D edge detection methods to define landmarks for point-based warping in autoradiographic brain imaging. In Bildverarbeitung fuer die Medizin (Leipzig), pp. 318-321.

Pielot R, Scholz M, Obermayer K, Gundelfinger ED and Hess A (2002). Performance of 3D landmark detection methods for point-based warping in autoradiographic brain imaging. IEEE Southwest Symposium on Image Analysis and Interpretation (Santa Fe, New Mexico: IEEE Computer Society): pp. 269-273.

Pielot R, Scholz M, Obermayer K, Gundelfinger ED and Hess A (2001). 3D-edge detection to define landmarks for point-based warping in brain imaging. ICIP 2001 Proceedings (Thessaloniki, Greece: IEEE Signal Processing Society): pp. 343-346. Fulltext

Pielot R, Scholz M, Obermayer K, Gundelfinger ED and Hess A (2000). Warping with optimized weighting factors of displacement vectors - a new method to reduce interindividual variations in brain imaging. 4th IEEE Southwest Symposium on Image Analysis and Interpretation (Austin, TX, USA: IEEE Computer Society): pp. 264-268.

Pielot R, Scholz M, Obermayer K, Gundelfinger ED and Hess A (2000). A new approach to define landmarks for point-based warping in brain imaging. In Bildverarbeitung fuer die Medizin, Horsch A and Lehmann T, eds. (Heidelberg: Springer-Verlag), pp. 28-32. Fulltext

Pielot R, Scholz M, Obermayer K, Gundelfinger ED and Hess A (2000). Point-based warping with optimized weighting factors of displacement vectors. Medical Imaging 2000. Hanson KM, ed. (San Diego, CA: SPIE) 3979: pp. 1387-1395. Fulltext

Pielot R, Gundelfinger ED and Hess A (1999). A new approach to define reference points between 3D brain image datasets in spatially-based warping. 1st Goettingen Conference of the German Neuroscience Society (Goettingen: Thieme): p. 909.

Pielot R, Scholz M, Obermayer K, Gundelfinger ED and Hess A (1999). Optimiertes Warping durch gewichtete Summen von Verschiebungsvektoren - eine neue Methode zur Reduktion von interindividuellen Variabilitaeten von Hirndaten. In Bildverarbeitung fuer die Medizin, Evers H, Glombitza G, Lehmann T and Meinzer HP, eds. (Berlin, Heidelberg, New York: Springer-Verlag), pp. 417-421. Fulltext