DNeuroMAT: A Deep-Learning-Based Neuron Morphology Analysis Toolbox.

Min Liu, Zhuangdian Lin, Weixun Chen, Erik Meijering, Yaonan Wang

Abstract read

In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Min LiuCollege of Electrical and Information Engineering, Hunan University, Hunan, China. liu_min@hnu.edu.cn.

Zhuangdian LinCollege of Electrical and Information Engineering, Hunan University, Hunan, China.

Weixun ChenCollege of Electrical and Information Engineering, Hunan University, Hunan, China.

Erik MeijeringSchool of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia.

Yaonan WangCollege of Electrical and Information Engineering, Hunan University, Hunan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Digital reconstruction of neuronal structures from 3D neuron microscopy images is critical for the quantitative investigation of brain circuits and functions. Currently, neuron reconstructions are mainly obtained by manual or semiautomatic methods. However, these ways are labor-intensive, especially when handling the huge volume of whole brain microscopy imaging data. Here, we present a deep-learning-based neuron morphology analysis toolbox (DNeuroMAT) for automated analysis of neuron microscopy images, which consists of three modules: neuron segmentation, neuron reconstruction, and neuron critical points detection.

Indexed as

Deep LearningImaging, Three-DimensionalNeuronsAnimalsBrainHumansImage Processing, Computer-AssistedSoftware3D neuron reconstructionCritical points detectionDeep learningImage analysisImage segmentationImage segmentation

Identifiers

PMID39134850

DOI10.1109/JBHI.2021.3124514

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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.