ArticleNeuroinformatics2015
Adaptive Image Enhancement for Tracing 3D Morphologies of Neurons and Brain Vasculatures.
Article in Neuroinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Towards next-generation diagnostic pathology: AI-empowered label-free multiphoton microscopy.Light, science & applications · 2024Review
- NIEND: neuronal image enhancement through noise disentanglement.Bioinformatics (Oxford, England) · 2024Article
- DDeep3M+: adaptive enhancement powered weakly supervised learning for neuron segmentation.Neurophotonics · 2023Article
- SNAP: a structure-based neuron morphology reconstruction automatic pruning pipeline.Frontiers in neuroinformatics · 2023Article
- Neuron tracing from light microscopy images: automation, deep learning and bench testing.Bioinformatics (Oxford, England) · 2022Review
- Smart imaging to empower brain-wide neuroscience at single-cell levels.Brain informatics · 2022Review
- Deep learning-based real-time detection of neurons in brain slices for in vitro physiology.Scientific reports · 2021Article
- Article
- Automated Neuron Tracing Using Content-Aware Adaptive Voxel Scooping on CNN Predicted Probability Map.Frontiers in neuroanatomy · 2021Article
- Classification of electrophysiological and morphological neuron types in the mouse visual cortex.Nature neuroscience · 2019Article
- Automated Neuron Reconstruction from 3D Fluorescence Microscopy Images Using Sequential Monte Carlo Estimation.Neuroinformatics · 2019Article
- ShuTu: Open-Source Software for Efficient and Accurate Reconstruction of Dendritic Morphology.Frontiers in neuroinformatics · 2019Article
- Large-scale Exploration of Neuronal Morphologies Using Deep Learning and Augmented Reality.Neuroinformatics · 2018Article
- Mild myelin disruption elicits early alteration in behavior and proliferation in the subventricular zone.eLife · 2018Article
- Soma Detection in 3D Images of Neurons using Machine Learning Technique.Neuroinformatics · 2018Article
- Automatic and adaptive heterogeneous refractive index compensation for light-sheet microscopy.Nature communications · 2017Article
- Quantifying three-dimensional rodent retina vascular development using optical tissue clearing and light-sheet microscopy.Journal of biomedical optics · 2017Article
- Ensemble Neuron Tracer for 3D Neuron Reconstruction.Neuroinformatics · 2017Article
- Rivulet: 3D Neuron Morphology Tracing with Iterative Back-Tracking.Neuroinformatics · 2016Article
- Adaptive and Background-Aware GAL4 Expression Enhancement of Co-registered Confocal Microscopy Images.Neuroinformatics · 2016Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
It is important to digitally reconstruct the 3D morphology of neurons and brain vasculatures. A number of previous methods have been proposed to automate the reconstruction process. However, in many cases, noise and low signal contrast with respect to the image background still hamper our ability to use automation methods directly. Here, we propose an adaptive image enhancement method specifically designed to improve the signal-to-noise ratio of several types of individual neurons and brain vasculature images. Our method is based on detecting the salient features of fibrous structures, e.g. the axon and dendrites combined with adaptive estimation of the optimal context windows where such saliency would be detected. We tested this method for a range of brain image datasets and imaging modalities, including bright-field, confocal and multiphoton fluorescent images of neurons, and magnetic resonance angiograms. Applying our adaptive enhancement to these datasets led to improved accuracy and speed in automated tracing of complicated morphology of neurons and vasculatures.
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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.