Abstract
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.
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Acknowledgments
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.
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We have no conflict of interest to declare.
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S. Conjeti and S. Mesbah contributed equally towards the work.
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Conjeti, S., Mesbah, S., Negahdar, M. et al. Neuron-Miner: An Advanced Tool for Morphological Search and Retrieval in Neuroscientific Image Databases. Neuroinform 14, 369–385 (2016). https://doi.org/10.1007/s12021-016-9300-2
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DOI: https://doi.org/10.1007/s12021-016-9300-2