Automated neuron tracing using probability hypothesis density filtering

Radojević, Miroslav; Meijering, Erik

doi:10.1093/bioinformatics/btw751

Cited by 40 publications

(22 citation statements)

References 62 publications

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“…The other four tracing methods have been applied on the original images. The tracing recall values of Neutube [17], Snake [18], PHDF [19], and NeuroGPS-Tree [8] methods are 0.86, 0.90, 0.99, and 0.97 respectively. In Fig.…”

Section: Resultsmentioning

confidence: 97%

Super-resolution segmentation network for reconstruction of packed neurites

Zhou

Quan

Huang

et al. 2020

Preprint

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Neuron reconstruction can provide the quantitative data required for measuring the neuronal morphology and is crucial in the field of brain research. However, the difficulty in reconstructing packed neuritis, wherein massive labor is required for accurate reconstruction in most cases, has not been resolved. In this work, we provide a fundamental pathway for solving this challenge by proposing the use of the super-resolution segmentation network (SRSNet) that builds the mapping of the neurites in the original neuronal images and their segmentation in a higher-resolution space. SRSNet focuses on enlarging the distances between the boundaries of the packed neurites producing the high-resolution segmentation images. Thus, in the construction of the training datasets, only the traced skeletons of neurites are required, which vastly increase the usability of SRSNet. From the results of the experiments conducted in this work, it has been observed that SRSNet achieves accurate reconstruction of packed neurites where the other state-of-the-art methods fail.

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Section: Resultsmentioning

confidence: 97%

Super-resolution segmentation network for reconstruction of packed neurites

Zhou

Quan

Huang

et al. 2020

Preprint

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“…This allows the manipulation of these structures within the framework of graph theory. As such, TraceMontage implements a fuzzy-logic (Radojevic and Meijering, 2017) inspired algorithm for the identification of overlapping branches. Additional, detailed information about the algorithms implemented in TraceMontage can be found in the Supplementary Methods.…”

Section: Algorithmsmentioning

confidence: 99%

TraceMontage: a Method for Merging Multiple Independent Neuronal Traces

Dizaji

Walker

Cai

2019

Preprint

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Background: The ability to reconstruct neuronal networks, local microcircuits, or the entire connectome is a central goal of modern neuroscience. Recently, advancements in sample preparation (e.g., sample expansion and Brainbow labeling) and optical (e.g., confocal and light sheet) techniques have enabled the imaging of increasingly large neural systems with high contrast. Tracing neuronal structures from these images proves challenging, however, necessitating tools that integrate multiple neuronal traces, potentially derived by various methods, into one combined (montaged) result.New Method: Here, we present TraceMontage, an ImageJ/Fiji plugin for the combination of multiple neuron traces of a single image, either redundantly or non-redundantly. Internally, it uses graph theory to connect topological patterns in the 3-D spatial coordinates of neuronal trees. The software generates a single output tracing file containing the montage traces of the input tracing files and provides several measures of consistency analysis among multiple tracers. Results and Comparison to existing method(s):To our knowledge, our software is the first dedicated method for the combination of tracing results. Combining multiple tracers increases the accuracy and speed of tracing of densely-labeled samples by harnessing collaborative effort. This utility is demonstrated using fluorescence microscope images from the hippocampus and primary visual cortex (V1) in Brainbow-labeled mice.Conclusions: TraceMontage provides researchers the ability to combine neuronal tracing data generated by either the same or different method(s). As datasets become larger, the ability to trace images in this parallel manner will help connectomics scale to increasingly larger neural systems.

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“…A C++ implementation of Rivulet2 is also available in Vaa3D as a neuron tracing plugin 2 . We compared Rivulet2 with several state-of-the-art neuron tracing methods, including APP2 [28], SmartTracing (SMART) [5], Farsight Snake (SNAKE) [16], Probability Hypothesis Density Filtering (PHD) [11], Ensemble Neuron Tracer [10], Neutube [7] and its predecessor Rivulet1 ( [8], [26]). We used the Vaa3D ported implementations for bench-marking the methods APP2, SMART, SNAKE, ENT, and Neutube.…”

Section: B Implementation and Evaluationmentioning

confidence: 99%

Automated 3D Neuron Tracing with Precise Branch Erasing and Confidence Controlled Back-Tracking

Liu

Zhang

Song

et al. 2017

Preprint

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The automatic reconstruction of single neuron cells from microscopic images is essential to enabling large-scale datadriven investigations in neuron morphology research. However, few previous methods were able to generate satisfactory results automatically from 3D microscopic images without human intervention. In this study, we developed a new algorithm for automatic 3D neuron reconstruction. The main idea of the proposed algorithm is to iteratively track backwards from the potential neuronal termini to the soma centre. An online confidence score is computed to decide if a tracing iteration should be stopped and discarded from the final reconstruction. The performance improvements comparing to the previous methods are mainly introduced by a more accurate estimation of the traced area and the confidence controlled back-tracking algorithm. The proposed algorithm supports large-scale batch-processing by requiring only one hyper-parameter for background segmentation. We benchtested the proposed algorithm on the images obtained from both the DIADEM challenge and the BigNeuron challenge. Our proposed algorithm achieved the state-of-the-art results.

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Automated neuron tracing using probability hypothesis density filtering

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 40 publications

References 62 publications

Super-resolution segmentation network for reconstruction of packed neurites

Super-resolution segmentation network for reconstruction of packed neurites

TraceMontage: a Method for Merging Multiple Independent Neuronal Traces

Automated 3D Neuron Tracing with Precise Branch Erasing and Confidence Controlled Back-Tracking

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