Improved blind demixing methods for recovering dense neuronal morphology from barcode imaging data

Cellular barcoding methods offer the exciting possibility of 'infinite-pseudocolor' anatomical reconstruction --- i.e., assigning each neuron its own random unique barcoded 'pseudocolor,' and then using these pseudocolors to trace the microanatomy of each neuron. Here we use simulations, based on densely-reconstructed electron microscopy microanatomy, with signal structure matched to real barcoding data, to quantify the feasibility of this procedure. We develop a new blind demixing approach to recover the barcodes that label each neuron. We also develop a neural network which uses these barcodes to reconstruct the neuronal morphology from the observed fluorescence imaging data, 'connecting the dots' between discontiguous amplicon signals.

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Centered Kernel Alignment Enhancing Neural Network Pretraining for MRI-Based Dementia Diagnosis

Computational and Mathematical Methods in Medicine ◽

10.1155/2016/9523849 ◽

2016 ◽

Vol 2016 ◽

pp. 1-10 ◽

Cited By ~ 10

Author(s):

David Cárdenas-Peña ◽

Diego Collazos-Huertas ◽

German Castellanos-Dominguez

Keyword(s):

Neural Network ◽

Characteristic Curve ◽

Principal Component ◽

Imaging Data ◽

Resonance Imaging ◽

Dementia Diagnosis ◽

Target Matrix ◽

Computer Aided ◽

Artificial Neural Network Ann ◽

Aided Diagnosis

Dementia is a growing problem that affects elderly people worldwide. More accurate evaluation of dementia diagnosis can help during the medical examination. Several methods for computer-aided dementia diagnosis have been proposed using resonance imaging scans to discriminate between patients with Alzheimer’s disease (AD) or mild cognitive impairment (MCI) and healthy controls (NC). Nonetheless, the computer-aided diagnosis is especially challenging because of the heterogeneous and intermediate nature of MCI. We address the automated dementia diagnosis by introducing a novel supervised pretraining approach that takes advantage of the artificial neural network (ANN) for complex classification tasks. The proposal initializes an ANN based on linear projections to achieve more discriminating spaces. Such projections are estimated by maximizing the centered kernel alignment criterion that assesses the affinity between the resonance imaging data kernel matrix and the label target matrix. As a result, the performed linear embedding allows accounting for features that contribute the most to the MCI class discrimination. We compare the supervised pretraining approach to two unsupervised initialization methods (autoencoders and Principal Component Analysis) and against the best four performing classification methods of the 2014CADDementiachallenge. As a result, our proposal outperforms all the baselines (7% of classification accuracy and area under the receiver-operating-characteristic curve) at the time it reduces the class biasing.

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An adaptive resonance theory based artificial neural network (ART-2a) for rapid identification of airborne particle shapes from their scanning electron microscopy images

Chemometrics and Intelligent Laboratory Systems ◽

10.1016/0169-7439(94)85054-2 ◽

1994 ◽

Vol 25 (2) ◽

pp. 367-387 ◽

Cited By ~ 15

Author(s):

Dietrich Wienke ◽

Ying Xie ◽

Philip K. Hopke

Keyword(s):

Neural Network ◽

Electron Microscopy ◽

Rapid Identification ◽

Adaptive Resonance Theory ◽

Airborne Particle ◽

Resonance Theory ◽

Adaptive Resonance ◽

Particle Shapes ◽

Microscopy Images ◽

Scanning Electron

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Gadospin F—Enhanced Magnetic Resonance Imaging for Diagnosis and Monitoring of Atherosclerosis: Validation with Transmission Electron Microscopy and X-Ray Fluorescence Imaging in the Apolipoprotein E—Deficient Mouse

Molecular Imaging ◽

10.2310/7290.2014.00039 ◽

2015 ◽

Vol 14 (1) ◽

pp. 7290.2014.00039

Author(s):

Caroline Jung ◽

Tanja Dučić ◽

Rudolph Reimer ◽

Eva Koziolek ◽

Fabian Kording ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Electron Microscopy ◽

Transmission Electron Microscopy ◽

Magnetic Resonance ◽

Apolipoprotein E ◽

Fluorescence Imaging ◽

Deficient Mouse ◽

Resonance Imaging ◽

X Ray ◽

Transmission Electron

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A Recurrent Neural Network Approach for Automated Neural Tracing in Multispectral 3D Images

10.1101/230441 ◽

2018 ◽

Author(s):

Yan Yan ◽

Douglas H. Roossien ◽

Benjamin V. Sadis ◽

Jason J. Corso ◽

Dawen Cai

Keyword(s):

Neural Network ◽

Deep Learning ◽

Neuronal Cell ◽

Ground Truth ◽

Neuronal Morphology ◽

Spectral Information ◽

Neural Net ◽

Learning Approaches ◽

3D Images ◽

Neural Tracing

AbstractNeuronal morphology reconstruction in fluorescence microscopy 3D images is essential for analyzing neuronal cell type and connectivity. Manual tracing of neurons in these images is time consuming and subjective. Automated tracing is highly desired yet is one of the foremost challenges in computational neuroscience. The multispectral labeling technique, Brainbow utilizes high dimensional spectral information to distinguish intermingled neuronal processes. It is particular interesting to develop new algorithms to include the spectral information into the tracing process. Recently, deep learning approaches achieved state-of-the-art in different computer vision and medical imaging applications. To benefit from the power of deep learning, in this paper, we propose an automated neural tracing approach in multispectral 3D Brainbow images based on recurrent neural net-work. We first adopt VBM4D approach to denoise multispectral 3D images. Then we generate cubes as training samples along the ground truth, manually traced paths. These cubes are the input to the recur-rent neural network. The proposed approach is simple and effective. The approach can be implemented with the deep learning toolbox ‘Keras’ in 100 lines. Finally, to evaluate our approach, we computed the average and standard deviation of DIADEM metric from the ground truth results to our tracing results, and from our tracing results to the ground truth results. Extensive experimental results on the collected dataset demonstrate that the proposed approach performs well in Brainbow labeled mouse brain images.

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Taxonomic synthesis of the eastern North American millipede genus Pseudopolydesmus (Diplopoda: Polydesmida: Polydesmidae), utilizing high-detail ultraviolet fluorescence imaging

Zoological Journal of the Linnean Society ◽

10.1093/zoolinnean/zlz020 ◽

2019 ◽

Vol 187 (1) ◽

pp. 117-142 ◽

Cited By ~ 1

Author(s):

Petra Sierwald ◽

Derek A Hennen ◽

Xavier J Zahnle ◽

Stephanie Ware ◽

Paul E Marek

Keyword(s):

Electron Microscopy ◽

Scanning Electron Microscopy ◽

Fluorescence Imaging ◽

North American ◽

Type Material ◽

High Quality ◽

Subjective Synonym ◽

Scanning Electron

Abstract The species of the eastern North American millipede genus Pseudopolydesmus are reviewed. Synonyms and comprehensive literature citations are provided for each of the eight recognized species. Diagnostic morphology of the genus, including clarification of male gonopod terminology, is reviewed and defined using scanning electron microscopy and high-quality macrophotographic images, including those in which ultraviolet fluorescence was induced to produce detailed images of morphological structures. Based on the examination of available type material, the following eight species are recognized: (1) Pseudopolydesmus erasus; (2) Pseudopolydesmus canadensis; (3) Pseudopolydesmus collinus; (4) Pseudopolydesmus pinetorum; (5) Pseudopolydesmus minor; (6) Pseudopolydesmus caddo; (7) Pseudopolydesmus paludicolus; and (8) Pseudopolydesmus serratus. The species names Polydesmus neoterus and Polydesmus euthetus are here placed as junior subjective synonyms of Ps. minor (both syn. nov.), and Polydesmus natchitoches is placed as a junior subjective synonym of Ps. pinetorum (syn. nov.).

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Tree Species Classification Based on Hybrid Ensembles of a Convolutional Neural Network (CNN) and Random Forest Classifiers

Remote Sensing ◽

10.3390/rs11232788 ◽

2019 ◽

Vol 11 (23) ◽

pp. 2788 ◽

Cited By ~ 4

Author(s):

Uwe Knauer ◽

Cornelius Styp von Rekowski ◽

Marianne Stecklina ◽

Tilman Krokotsch ◽

Tuan Pham Minh ◽

...

Keyword(s):

Neural Network ◽

Random Forest ◽

Convolutional Neural Network ◽

Tree Species ◽

Classification Performance ◽

Majority Voting ◽

Imaging Data ◽

Species Classification ◽

Weighted Voting ◽

Linear Discriminant

In this paper, we evaluate different popular voting strategies for fusion of classifier results. A convolutional neural network (CNN) and different variants of random forest (RF) classifiers were trained to discriminate between 15 tree species based on airborne hyperspectral imaging data. The spectral data was preprocessed with a multi-class linear discriminant analysis (MCLDA) as a means to reduce dimensionality and to obtain spatial–spectral features. The best individual classifier was a CNN with a classification accuracy of 0.73 +/− 0.086. The classification performance increased to an accuracy of 0.78 +/− 0.053 by using precision weighted voting for a hybrid ensemble of the CNN and two RF classifiers. This voting strategy clearly outperformed majority voting (0.74), accuracy weighted voting (0.75), and presidential voting (0.75).

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