SynQuant: an automatic tool to quantify synapses from microscopy images

AbstractMotivationSynapses are essential to neural signal transmission. Therefore, quantification of synapses and related neurites from images is vital to gain insights into the underlying pathways of brain functionality and diseases. Despite the wide availability of synapse imaging data, several issues prevent satisfactory quantification of these structures by current tools. First, the antibodies used for labeling synapses are not perfectly specific to synapses. These antibodies may exist in neurites or other cell compartments. Second, the brightness for different neurites and synapses is heterogeneous due to the variation of antibody concentration and synapse-intrinsic differences. Third, images often have low signal to noise ratio (SNR) due to constraints of experiments and availability of sensitive antibodies. The combination of these issues makes the detection of synapses challenging and necessitates developing a new tool to accurately and reliably quantify synapses.ResultsWe present an automatic probability-principled synapse detection algorithm and integrate it into our synapse quantification tool SynQuant. Derived from the theory of order statistics, our method controls the false discovery rate and improves the power of detecting synapses. Through extensive experiments on both synthetic and real images in the presence of severe antibody diffusion, high heterogeneity, and large noise, our method was demonstrated to outperform peer specialized synapse detection tools as well as generic spot detection methods by a large margin. Finally, we show SynQuant reliably uncovers statistically significant differences between disease and control conditions in a neuron-astrocyte co-culture based model of Down Syndrome.AvailabilityThe Java source code, Fiji plug-in, and test data are available at https://github.com/yu-lab-vt/[email protected]

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Robust PPG Peak Detection Using Dilated Convolutional Neural Networks

10.36227/techrxiv.16529310.v2 ◽

2021 ◽

Author(s):

Kianoosh Kazemi ◽

Juho Laitala ◽

Iman Azimi ◽

Pasi Liljeberg ◽

Amir M. Rahmani

Keyword(s):

Neural Networks ◽

Heart Rate ◽

Convolutional Neural Networks ◽

Signal To Noise Ratio ◽

Motion Artifact ◽

Detection Algorithm ◽

Peak Detection ◽

Detection Methods ◽

Method Performance ◽

Data Generator

<div>Accurate peak determination from noise-corrupted photoplethysmogram (PPG) signal is the basis for further analysis of physiological quantities such as heart rate and heart rate variability. In the past decades, many methods have been proposed to provide reliable peak detection. These peak detection methods include rule-based algorithms, adaptive thresholds, and signal processing techniques. However, they are designed for noise-free PPG signals and are insufficient for PPG signals with low signal-to-noise ratio (SNR). This paper focuses on enhancing PPG noise-resiliency and proposes a robust peak detection algorithm for noise and motion artifact corrupted PPG signals. Our algorithm is based on Convolutional Neural Networks (CNN) with dilated convolutions. Using dilated convolutions provides a large receptive field, making our CNN model robust at time series processing. In this study, we use a dataset collected from wearable devices in health monitoring under free-living conditions. In addition, a data generator is developed for producing noisy PPG data used for training the network. The method performance is compared against other state-of-the-art methods and tested in SNRs ranging from 0 to 45 dB. Our method obtains better accuracy in all the SNRs, compared with the existing adaptive threshold and transform-based methods. The proposed method shows an overall precision, recall, and F1-score 80%, 80%, and 80% in all the SNR ranges. However, these figures for the other methods are below 78%, 77%, and 77%, respectively. The proposed method proves to be accurate for detecting PPG peaks even in the presence of noise.</div>

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Efficient implementation of convolutional neural networks in the data processing of two-photon in vivo imaging

Bioinformatics ◽

10.1093/bioinformatics/btz055 ◽

2019 ◽

Vol 35 (17) ◽

pp. 3208-3210 ◽

Cited By ~ 1

Author(s):

Yangzhen Wang ◽

Feng Su ◽

Shanshan Wang ◽

Chaojuan Yang ◽

Yonglu Tian ◽

...

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Detection Methods ◽

Supplementary Information ◽

Imaging Data ◽

Two Photon ◽

Processing Power ◽

Fluctuation Method ◽

The Brain

Abstract Motivation Functional imaging at single-neuron resolution offers a highly efficient tool for studying the functional connectomics in the brain. However, mainstream neuron-detection methods focus on either the morphologies or activities of neurons, which may lead to the extraction of incomplete information and which may heavily rely on the experience of the experimenters. Results We developed a convolutional neural networks and fluctuation method-based toolbox (ImageCN) to increase the processing power of calcium imaging data. To evaluate the performance of ImageCN, nine different imaging datasets were recorded from awake mouse brains. ImageCN demonstrated superior neuron-detection performance when compared with other algorithms. Furthermore, ImageCN does not require sophisticated training for users. Availability and implementation ImageCN is implemented in MATLAB. The source code and documentation are available at https://github.com/ZhangChenLab/ImageCN. Supplementary information Supplementary data are available at Bioinformatics online.

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dNEMO: a tool for quantification of mRNA and punctate structures in time-lapse images of single cells

Bioinformatics ◽

10.1093/bioinformatics/btaa874 ◽

2020 ◽

Author(s):

Gabriel J Kowalczyk ◽

J Agustin Cruz ◽

Yue Guo ◽

Qiuhong Zhang ◽

Natalie Sauerwald ◽

...

Keyword(s):

User Interface ◽

Single Cells ◽

Time Lapse ◽

Detection Algorithm ◽

Supplementary Information ◽

Imaging Data ◽

Molecular Assemblies ◽

Time Interaction ◽

Inflammatory Stimuli ◽

Fixed Cell

Abstract Motivation Many biological processes are regulated by single molecules and molecular assemblies within cells that are visible by microscopy as punctate features, often diffraction limited. Here, we present detecting-NEMO (dNEMO), a computational tool optimized for accurate and rapid measurement of fluorescent puncta in fixed-cell and time-lapse images. Results The spot detection algorithm uses the à trous wavelet transform, a computationally inexpensive method that is robust to imaging noise. By combining automated with manual spot curation in the user interface, fluorescent puncta can be carefully selected and measured against their local background to extract high-quality single-cell data. Integrated into the workflow are segmentation and spot-inspection tools that enable almost real-time interaction with images without time consuming pre-processing steps. Although the software is agnostic to the type of puncta imaged, we demonstrate dNEMO using smFISH to measure transcript numbers in single cells in addition to the transient formation of IKK/NEMO puncta from time-lapse images of cells exposed to inflammatory stimuli. We conclude that dNEMO is an ideal user interface for rapid and accurate measurement of fluorescent molecular assemblies in biological imaging data. Availability and implementation The data and software are freely available online at https://github.com/recleelab. Supplementary information Supplementary data are available at Bioinformatics online.

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Robust PPG Peak Detection Using Dilated Convolutional Neural Networks

10.36227/techrxiv.16529310 ◽

2021 ◽

Author(s):

Kianoosh Kazemi ◽

Juho Laitala ◽

Iman Azimi ◽

Pasi Liljeberg ◽

Amir M. Rahmani

Keyword(s):

Neural Networks ◽

Heart Rate ◽

Convolutional Neural Networks ◽

Signal To Noise Ratio ◽

Motion Artifact ◽

Detection Algorithm ◽

Peak Detection ◽

Detection Methods ◽

Method Performance ◽

Data Generator

<div>Accurate peak determination from noise-corrupted photoplethysmogram (PPG) signal is the basis for further analysis of physiological quantities such as heart rate and heart rate variability. In the past decades, many methods have been proposed to provide reliable peak detection. These peak detection methods include rule-based algorithms, adaptive thresholds, and signal processing techniques. However, they are designed for noise-free PPG signals and are insufficient for PPG signals with low signal-to-noise ratio (SNR). This paper focuses on enhancing PPG noise-resiliency and proposes a robust peak detection algorithm for noise and motion artifact corrupted PPG signals. Our algorithm is based on Convolutional Neural Networks (CNN) with dilated convolutions. Using dilated convolutions provides a large receptive field, making our CNN model robust at time series processing. In this study, we use a dataset collected from wearable devices in health monitoring under free-living conditions. In addition, a data generator is developed for producing noisy PPG data used for training the network. The method performance is compared against other state-of-the-art methods and tested in SNRs ranging from 0 to 45 dB. Our method obtains better accuracy in all the SNRs, compared with the existing adaptive threshold and transform-based methods. The proposed method shows an overall precision, recall, and F1-score 80%, 80%, and 80% in all the SNR ranges. However, these figures for the other methods are below 78%, 77%, and 77%, respectively. The proposed method proves to be accurate for detecting PPG peaks even in the presence of noise.</div>

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Inferring subgroup-specific driver genes from heterogeneous cancer samples via subspace learning with subgroup indication

Bioinformatics ◽

10.1093/bioinformatics/btz793 ◽

2019 ◽

Cited By ~ 2

Author(s):

Jianing Xi ◽

Xiguo Yuan ◽

Minghui Wang ◽

Ao Li ◽

Xuelong Li ◽

...

Keyword(s):

Ground Truth ◽

Subspace Learning ◽

Detection Methods ◽

Supplementary Information ◽

Driver Gene ◽

Key Factors ◽

Gene Detection ◽

Driver Genes ◽

Learning Framework ◽

Mutation Data

AbstractMotivationDetecting driver genes from gene mutation data is a fundamental task for tumorigenesis research. Due to the fact that cancer is a heterogeneous disease with various subgroups, subgroup-specific driver genes are the key factors in the development of precision medicine for heterogeneous cancer. However, the existing driver gene detection methods are not designed to identify subgroup specificities of their detected driver genes, and therefore cannot indicate which group of patients is associated with the detected driver genes, which is difficult to provide specifically clinical guidance for individual patients.ResultsBy incorporating the subspace learning framework, we propose a novel bioinformatics method called DriverSub, which can efficiently predict subgroup-specific driver genes in the situation where the subgroup annotations are not available. When evaluated by simulation datasets with known ground truth and compared with existing methods, DriverSub yields the best prediction of driver genes and the inference of their related subgroups. When we apply DriverSub on the mutation data of real heterogeneous cancers, we can observe that the predicted results of DriverSub are highly enriched for experimentally validated known driver genes. Moreover, the subgroups inferred by DriverSub are significantly associated with the annotated molecular subgroups, indicating its capability of predicting subgroup-specific driver genes.Availability and implementationThe source code is publicly available at https://github.com/JianingXi/DriverSub.Supplementary informationSupplementary data are available at Bioinformatics online.

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High precision automated detection of labeled nuclei in Gigapixel resolution image data of Mouse Brain

10.1101/252247 ◽

2018 ◽

Cited By ~ 2

Author(s):

Sukhendu Das ◽

Jaikishan Jayakumar ◽

Samik Banerjee ◽

Janani Ramaswamy ◽

Venu Vangala ◽

...

Keyword(s):

Mouse Brain ◽

High Performance ◽

Feature Detection ◽

Human Performance ◽

Fluorescent Protein ◽

Ground Truth ◽

Classification Problem ◽

Complex Problem ◽

Detection Methods ◽

Imaging Data

AbstractThere is a need in modern neuroscience for accurate and automated image processing techniques for analyzing the large volume of neuroanatomical imaging data. Even at light microscopic levels, imaging mouse brains produces individual data volumes in the TerraByte range. A fundamental task involves the detection and quantification of objects of a given type, e.g. neuronal nuclei or somata, in brain scan dataset. Traditionally this quantification has been performed by human visual inspection with high accuracy, that is not scalable. When modern automated CNN and SVM-based methods are used to solve this classification problem, they achieve accuracy levels that range between 85 – 92%. However, higher rates of precision and recall that are close to that of human performance are necessary. In this paper, we describe an unsupervised, iterative algorithm, which provides a high performance for a specific problem of detecting Green Fluorescent Protein labeled nuclei in 2D scans of mouse brains. The algorithm judiciously combines classical computer vision techniques and is focused on the complex problem of decomposing strong overlapped objects of interest. Our proposed technique uses feature detection methods on ridge lines over distance transformation of the image and an arc based iterative spatial-filling method to solve the problem. We demonstrate our results on mouse brain dataset of Gigabyte resolution and compare it with manual annotation of the brains. Our results show that an aptly designed CV algorithm with classical feature extractors when tailored to this problem of interest achieves near-ideal human-like performance. Quantitative comparative analysis, using manually annotated ground truth, reveals that our approach performs better on mouse brain scans than general purpose machine learning (including deep CNN) methods.

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Robust PPG Peak Detection Using Dilated Convolutional Neural Networks

10.36227/techrxiv.16529310.v1 ◽

2021 ◽

Author(s):

Kianoosh Kazemi ◽

Juho Laitala ◽

Iman Azimi ◽

Pasi Liljeberg ◽

Amir M. Rahmani

Keyword(s):

Neural Networks ◽

Heart Rate ◽

Convolutional Neural Networks ◽

Signal To Noise Ratio ◽

Motion Artifact ◽

Detection Algorithm ◽

Peak Detection ◽

Detection Methods ◽

Method Performance ◽

Data Generator

<div>Accurate peak determination from noise-corrupted photoplethysmogram (PPG) signal is the basis for further analysis of physiological quantities such as heart rate and heart rate variability. In the past decades, many methods have been proposed to provide reliable peak detection. These peak detection methods include rule-based algorithms, adaptive thresholds, and signal processing techniques. However, they are designed for noise-free PPG signals and are insufficient for PPG signals with low signal-to-noise ratio (SNR). This paper focuses on enhancing PPG noise-resiliency and proposes a robust peak detection algorithm for noise and motion artifact corrupted PPG signals. Our algorithm is based on Convolutional Neural Networks (CNN) with dilated convolutions. Using dilated convolutions provides a large receptive field, making our CNN model robust at time series processing. In this study, we use a dataset collected from wearable devices in health monitoring under free-living conditions. In addition, a data generator is developed for producing noisy PPG data used for training the network. The method performance is compared against other state-of-the-art methods and tested in SNRs ranging from 0 to 45 dB. Our method obtains better accuracy in all the SNRs, compared with the existing adaptive threshold and transform-based methods. The proposed method shows an overall precision, recall, and F1-score 80%, 80%, and 80% in all the SNR ranges. However, these figures for the other methods are below 78%, 77%, and 77%, respectively. The proposed method proves to be accurate for detecting PPG peaks even in the presence of noise.</div>

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Object Detection Algorithm Based on Improved YOLOv3

Electronics ◽

10.3390/electronics9030537 ◽

2020 ◽

Vol 9 (3) ◽

pp. 537 ◽

Cited By ~ 5

Author(s):

Liquan Zhao ◽

Shuaiyang Li

Keyword(s):

Markov Chains ◽

Object Detection ◽

Large Scale ◽

Ground Truth ◽

Detection Algorithm ◽

Detection Methods ◽

Cluster Method ◽

Cluster Center ◽

Initial Cluster ◽

Bounding Boxes

The ‘You Only Look Once’ v3 (YOLOv3) method is among the most widely used deep learning-based object detection methods. It uses the k-means cluster method to estimate the initial width and height of the predicted bounding boxes. With this method, the estimated width and height are sensitive to the initial cluster centers, and the processing of large-scale datasets is time-consuming. In order to address these problems, a new cluster method for estimating the initial width and height of the predicted bounding boxes has been developed. Firstly, it randomly selects a couple of width and height values as one initial cluster center separate from the width and height of the ground truth boxes. Secondly, it constructs Markov chains based on the selected initial cluster and uses the final points of every Markov chain as the other initial centers. In the construction of Markov chains, the intersection-over-union method is used to compute the distance between the selected initial clusters and each candidate point, instead of the square root method. Finally, this method can be used to continually update the cluster center with each new set of width and height values, which are only a part of the data selected from the datasets. Our simulation results show that the new method has faster convergence speed for initializing the width and height of the predicted bounding boxes and that it can select more representative initial widths and heights of the predicted bounding boxes. Our proposed method achieves better performance than the YOLOv3 method in terms of recall, mean average precision, and F1-score.

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Detecting Ephemeral Objects in SAR Time-Series Using Frozen Background-Based Change Detection

Remote Sensing ◽

10.3390/rs12111720 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1720

Author(s):

Thibault Taillade ◽

Laetitia Thirion-Lefevre ◽

Régis Guinvarc’h

Keyword(s):

Change Detection ◽

Signal To Noise Ratio ◽

Open Data ◽

Ground Truth ◽

Critical Issue ◽

Detection Methods ◽

Reference Image ◽

Radar Images ◽

Multi Temporal ◽

Disaster Monitoring

Change detection (CD) in SAR (Synthethic Aperture Radar) images has been widely studied in recent years and has become increasingly attractive due to the growth of available datasets. The potential of CD has been shown in different fields, including disaster monitoring and military applications. Access to multi-temporal SAR images of the same scene is now possible, and therefore we can improve the performance and the interpretation of CD. Apart from specific SAR campaign measurements, the ground truth of the scene is usually unknown or only partially known when dealing with open data. This is a critical issue when the purpose is to detect targets, such as vehicles or ships. Indeed, typical change detection methods can only provide relative changes; the actual number of targets on each day cannot be determined. Ideally, this change detection should occur between a target-free image and one with the objects of interest. To do so, we propose to benefit from pixels’ intrinsic temporal behavior to compute a frozen background reference (FBR) image and perform change detection from this reference image. We will then consider that the scene consists only of immobile objects (e.g., buildings and trees) and removable objects that can appear and disappear from acquisition to another (e.g., cars and ships). Our FBR images will, therefore, aim to estimate the immobile background of the scene to obtain, after change detection, the exact amount of targets present on each day. This study was conducted first with simulated SAR data for different number of acquisition dates and Signal-to-Noise Ratio (SNR). We presented an application in the region of Singapore to estimate the number of ships in the study area for each acquisition.

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