Structure-Based Neuron Retrieval Across Drosophila Brains
Comparing local neural structures across large sets of examples is crucial when studying gene functions, and their effect in the Drosophila brain. The current practice of aligning brain volume data to a joint reference frame is based on the neuropil. However, even after alignment neurons exhibit residual location and shape variability that, together with image noise, hamper direct quantitative comparison and retrieval of similar structures on an intensity basis. In this paper, we propose and evaluate an image-based retrieval method for neurons, relying on local appearance, which can cope with spatial variability across the population. For an object of interest marked in a query case, the method ranks cases drawn from a large data set based on local neuron appearance in confocal microscopy data. The approach is based on capturing the orientation of neurons based on structure tensors and expanding this field via Gradient Vector Flow. During retrieval, the algorithm compares fields across cases, and calculates a corresponding ranking of most similar cases with regard to the local structure of interest. Experimental results demonstrate that the similarity measure and ranking mechanisms yield high precision and recall in realistic search scenarios.
KeywordsDrosophila Neuron retrieval Structure tensor Similarity measure Gradient vector flow
This work was partly funded by the European Union (257528, KHRESMOI, 330003 FABRIC, 318068 VISCERAL), the Austrian Sciences Fund (P 22578-B19, PULMARCH) the OeNB (14812, FETALMORPHO) and the FFG Headquarter Project ”Molecular Basis” (Grant number: 834223).
- Birngruber, E., Langs, G., Donner, R. (2009). matVTK - 3D visualization for MATLAB. The MIDAS Journal, pp. 1–8.Google Scholar
- Brand, H., & Perrimon, N. (1993). Targeted gene expression as a means of altering cell fates and generating dominant phenotypes. Development (Cambridge, England), 118(2), 401–415.Google Scholar
- Chigirev, D., & Bialek, W. (2004). Optimal manifold representation of data: an information theoretic approach. Advances in Neural Information Processing Systems, 16, 161.Google Scholar
- Dickson, B. (2012). Website of Dickson Group.Google Scholar
- Dittrich, E. (2009). Automatic segmentation of retinal vessels and measurement of doppler flow velocity in optical coherence tomography data. Diploma thesis, Vienna University of Technology.Google Scholar
- Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A. (1998). Multiscale vessel enhancement filtering. Medical image computing and computer-assisted intervention: MICCAI... International Conference on Medical Image Computing and Computer-Assisted Intervention, 1496, 130–137.Google Scholar
- Knutsson, H. (1989). Representing local structure using tensors. The 6th Scandinavian conference on image analysis (pp. 244–251).Google Scholar
- Knutsson, H., Westin, C., Andersson, M. (2011). Representing local structure using tensors II. Image Analysis Lecture Notes in Computer Science, 6688, 545–556. http://link.springer.com/chapter/10.1007%2F978-3-642-21227-7_51
- Nicolaï, L.J.J., Ramaekers, A., Raemaekers, T., Drozdzecki, A., Mauss, A.S., Yan, J., Landgraf, M., Annaert, W., Hassan, B.A. (2010). Genetically encoded dendritic marker sheds light on neuronal connectivity in Drosophila. Proceedings of the National Academy of Sciences of the United States of America, 107(47), 20553–20558.PubMedCentralPubMedCrossRefGoogle Scholar
- Rohlfing, T., & Maurer, C.R. (2003). Nonrigid image registration in shared-memory multiprocessor environments with application to brains, breasts, and bees. IEEE Transactions on Information Technology in Biomedicine: A Publication of the IEEE Engineering in Medicine and Biology Society, 7(1), 16–25.CrossRefGoogle Scholar
- Shapiro, L., & Stockman, G. (2002). Computer vision. Texts in computer science. Prentice Hall.Google Scholar
- Stalling, D., Westerhoff, M., Hege, H.-C. (2005). Amira: a highly interactive system for visual data analysis.Google Scholar