ArticleNeuroinformatics2018
Large-scale Exploration of Neuronal Morphologies Using Deep Learning and Augmented Reality.
Article in Neuroinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- SPCNNet: spiking point cloud neural network for morphological neuron classification.Scientific reports · 2026Article
- Mixed Reality (Holography)-Guided Minimally Invasive Cardiac Surgery-A Novel Comparative Feasibility Study.Journal of cardiovascular development and disease · 2025Article
- Multi-level feature fusion network for neuronal morphology classification.Frontiers in neuroscience · 2024Article
- Multi-gate Weighted Fusion Network for neuronal morphology classification.Frontiers in neuroscience · 2024Article
- Augmented reality during parotid surgery: real-life evaluation of voice control of a head mounted display.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2023Article
- NeuRegenerate: A Framework for Visualizing Neurodegeneration.IEEE transactions on visualization and computer graphics · 2023Article
- Highlights from the Era of Open Source Web-Based Tools.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2021Review
- A Novel Precise Optical Navigation System for Craniomaxillofacial Surgery Registered With an Occlusal Splint.The Journal of craniofacial surgeryArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Recently released large-scale neuron morphological data has greatly facilitated the research in neuroinformatics. However, the sheer volume and complexity of these data pose significant challenges for efficient and accurate neuron exploration. In this paper, we propose an effective retrieval framework to address these problems, based on frontier techniques of deep learning and binary coding. For the first time, we develop a deep learning based feature representation method for the neuron morphological data, where the 3D neurons are first projected into binary images and then learned features using an unsupervised deep neural network, i.e., stacked convolutional autoencoders (SCAEs). The deep features are subsequently fused with the hand-crafted features for more accurate representation. Considering the exhaustive search is usually very time-consuming in large-scale databases, we employ a novel binary coding method to compress feature vectors into short binary codes. Our framework is validated on a public data set including 58,000 neurons, showing promising retrieval precision and efficiency compared with state-of-the-art methods. In addition, we develop a novel neuron visualization program based on the techniques of augmented reality (AR), which can help users take a deep exploration of neuron morphologies in an interactive and immersive manner.
Indexed as
Identifiers
29435954What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.