Dataset Information

A hierarchically annotated dataset drives tangled filament recognition in digital neuron reconstruction.

ABSTRACT: Reconstructing neuronal morphology is vital for classifying neurons and mapping brain connectivity. However, it remains a significant challenge due to its complex structure, dense distribution, and low image contrast. In particular, AI-assisted methods often yield numerous errors that require extensive manual intervention. Therefore, reconstructing hundreds of neurons is already a daunting task for general research projects. A key issue is the lack of specialized training for challenging regions due to inadequate data and training methods. This study extracted 2,800 challenging neuronal blocks and categorized them into multiple density levels. Furthermore, we enhanced images using an axial continuity-based network that improved three-dimensional voxel resolution while reducing the difficulty of neuron recognition. Comparing the pre- and post-enhancement results in automatic algorithms using fluorescence micro-optical sectioning tomography (fMOST) data, we observed a significant increase in the recall rate. Our study not only enhances the throughput of reconstruction but also provides a fundamental dataset for tangled neuron reconstruction.

SUBMITTER: Chen W

PROVIDER: S-EPMC11368685 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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A hierarchically annotated dataset drives tangled filament recognition in digital neuron reconstruction.

Chen Wu W Liao Mingwei M Bao Shengda S An Sile S Li Wenwei W Liu Xin X Huang Ganghua G Gong Hui H Luo Qingming Q Xiao Chi C Li Anan A

Patterns (New York, N.Y.) 20240621 8

Reconstructing neuronal morphology is vital for classifying neurons and mapping brain connectivity. However, it remains a significant challenge due to its complex structure, dense distribution, and low image contrast. In particular, AI-assisted methods often yield numerous errors that require extensive manual intervention. Therefore, reconstructing hundreds of neurons is already a daunting task for general research projects. A key issue is the lack of specialized training for challenging regions ...[more]

PMID: 39233689

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Project description:The real-time detection of multinational banknotes remains an ongoing research challenge within the academic community. Numerous studies have been conducted to address the need for rapid and accurate banknote recognition, counterfeit detection, and identification of damaged banknotes [1], [2], [3]. State-of-the-art techniques, such as machine learning (ML) and deep learning (DL), have supplanted traditional digital image processing methods in banknote recognition and classification. However, the success of ML or DL projects critically hinges on the size and comprehensiveness of the datasets employed. Existing datasets suffer from several limitations. Firstly, there is a notable absence of a Peruvian banknote dataset suitable for training ML or DL models. Second, the lack of annotated data with specific labels and metadata for Peruvian currency hinders the development of effective supervised learning models for banknote recognition and classification. Lastly, datasets from different regions may not align with the unique characteristics, design, and security features of Peruvian banknotes, limiting the accuracy and applicability of models in a Peruvian context [4] To address these limitations, we have meticulously curated a comprehensive dataset comprising a total of 9,315 images of Peruvian banknotes, encompassing both old and new denominations from 2011 (old) and 2019 (new) [5]. The Peruvian banknote dataset includes denominations of 10, 20, 50, and 100 Peruvian soles. Importantly, as indicated by [5], both the 2011 and 2019 families of banknotes are currently in circulation, further enhancing the dataset's relevance for real-world applications in currency recognition and verification. This dataset serves as a vital resource for addressing the challenges in real-time multinational banknote detection. By offering a comprehensive collection of images of Peruvian banknotes, both old and new, this dataset fills a critical gap in the field of banknote recognition. Researchers can utilize it to train and evaluate advanced machine learning and deep learning models, ultimately enhancing the accuracy of banknote processing systems.

Dataset Information

A hierarchically annotated dataset drives tangled filament recognition in digital neuron reconstruction.

Publications

A hierarchically annotated dataset drives tangled filament recognition in digital neuron reconstruction.

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