Neuron-Miner: An Advanced Tool for Morphological Search and Retrieval in Neuroscientific Image Databases
- 623 Downloads
The steadily growing amounts of digital neuroscientific data demands for a reliable, systematic, and computationally effective retrieval algorithm. In this paper, we present Neuron-Miner, which is a tool for fast and accurate reference-based retrieval within neuron image databases. The proposed algorithm is established upon hashing (search and retrieval) technique by employing multiple unsupervised random trees, collectively called as Hashing Forests (HF). The HF are trained to parse the neuromorphological space hierarchically and preserve the inherent neuron neighborhoods while encoding with compact binary codewords. We further introduce the inverse-coding formulation within HF to effectively mitigate pairwise neuron similarity comparisons, thus allowing scalability to massive databases with little additional time overhead. The proposed hashing tool has superior approximation of the true neuromorphological neighborhood with better retrieval and ranking performance in comparison to existing generalized hashing methods. This is exhaustively validated by quantifying the results over 31266 neuron reconstructions from Neuromorpho.org dataset curated from 147 different archives. We envisage that finding and ranking similar neurons through reference-based querying via Neuron Miner would assist neuroscientists in objectively understanding the relationship between neuronal structure and function for applications in comparative anatomy or diagnosis.
KeywordsNeuroscientific databases Data mining Hashing Neuromorphological space Random Forests
We thank Ajayrama Kumaraswamy, Computational Neuroscience Department Biology II, Ludwigs Maximillian Universität München, Germany for insightful discussion in the early conception of this work. We thank the assistance of Bastien Saquet of Max Plank Digital Library, München, Germany in maintaining the web-service. We would like to thank the Max Plank Digital Library, München, Germany for providing computing resources for hosting the Neuron-Miner software and making it publicly accessible.
Conflict of interests
We have no conflict of interest to declare.
(WMV 3.49 MB)
- Albalate, A., & Suendermann, D. (2009). A combination approach to cluster validation based on statistical quantiles. In 2009. IJCBS’09. International Joint Conference on (pp. 549-555) Bioinformatics, Systems Biology and Intelligent Computing: IEEE.Google Scholar
- Costa, M., Ostrovsky, A.D., Manton, J.D., Prohaska, S., & Jefferis, G.S. (2014). NBLAST: Rapid, sensitive comparison of neuronal structure and construction of neuron family databases. bioRxiv, p.006346.Google Scholar
- Desgraupes, B. (2013). Clustering indices. University of Paris Ouest-Lab Modal’X, 1, 34.Google Scholar
- Gionis, A., Indyk, P., & Motwani, R. (1999). Similarity search in high dimensions via hashing. In VLDB 99(6), p. 518-529. Vancouver.Google Scholar
- He, X., Cai, D., Yan, S., & Zhang, H.J. (2005). Neighborhood preserving embedding. In 2005. ICCV 2005. Tenth IEEE International Conference on (Vol. 2, pp. 1208-1213) Computer Vision: IEEE.Google Scholar
- Joly, A., & Buisson, O. (2011). Random maximum margin hashing. In 2011 IEEE Conference on (pp. 873-880) Computer Vision and Pattern Recognition (CVPR): IEEE.Google Scholar
- Kovács, F., Legány, C., & Babos, A. (2005). Cluster validity measurement techniques. In Proceedings of the 6th International Symposium of Hungarian Researchers on Computational Intelligence (pp. 18–19). Budapest.Google Scholar
- Literature Search Main Results (2015). Available at: http://neuromorpho.org/neuroMorpho/LS_queryStatus.jsp, (Accessed: 09 February 2016).
- Liu, X., Huang, L., Deng, C., Lu, J., & Lang, B. (2015). Multi-View Complementary hash tables for nearest neighbor search. In Proceedings of the IEEE International Conference on Computer Vision (pp. 1107–1115).Google Scholar
- Louppe, G. (2014). Understanding random forests: From theory to practice.arXiv preprint arXiv:1407.7502.
- Menze, B.H., Kelm, B.M., Splitthoff, D.N., Koethe, U., & Hamprecht, F.A. (2011). On oblique random forests. In Machine Learning and Knowledge Discovery in Databases (pp. 453-469): Springer Berlin Heidelberg.Google Scholar
- Mesbah, S., Conjeti, S., Kumaraswamy, A., Rautenberg, P., Navab, N., & Katouzian, A. (2015). Hashing Forests for Morphological Search and Retrieval in Neuroscientific Image Databases. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015 (pp. 135-143): Springer International Publishing.Google Scholar
- Overview of L-Measure (2015). Available at: http://cng.gmu.edu:8080/Lm/help/index.htm, (Accessed: 09 February 2016).
- Rautenberg, P.L., Kumaraswamy, A., Tejero-Cantero, A., Doblander, C., Norouzian, M.R., Kai, K., Jacobsen, H.A., Ai, H., Wachtler, T., & Ikeno, H. (2014). Neurondepot: keeping your colleagues in sync by combining modern cloud storage services, the local file system, and simple web applications. Frontiers in Neuroinformatics, 8, 55.CrossRefPubMedPubMedCentralGoogle Scholar
- Search by Morphometry (2015). Available at: http://neuromorpho.org/neuroMorpho/MorphometrySearch.jsp.
- Scikit-learn: machine learning in Python – scikit-learn 0.16.1 documentation (2015) Available at: http://scikit-learn.org/stable/ (Accessed: 25 August 2015).
- Weiss, Y., Fergus, R., & Torralba, A. (2012). Multidimensional spectral hashing. In Computer Vision–ECCV 2012 (pp. 340–353): Springer Berlin Heidelberg.Google Scholar
- Yu, G., & Yuan, J. (2014). Scalable forest hashing for fast similarity search. In 2014 IEEE International Conference on (pp. 1-6) Multimedia and Expo (ICME): IEEE.Google Scholar
- Zhang, D., Wang, J., Cai, D., & Lu, J. (2010). Self-taught hashing for fast similarity search. In proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval (pp. 18-25): ACM.Google Scholar