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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets by Linus Manubens-Gil; Zhi Zhou; Hanbo Chen; Arvind Ramanathan; Xiaoxiao Liu; Yufeng Liu; Alessandro Bria; Todd Gillette; Zongcai Ruan; Jian Yang; Miroslav Radojević; Ting Zhao; Li Cheng; Lei Qu; Siqi Liu; Kristofer E. Bouchard; Lin Gu; Weidong Cai; Shuiwang Ji; Badrinath Roysam; Ching-Wei Wang; Hongchuan Yu; Amos Sironi; Daniel Maxim Iascone; Jie Zhou; Erhan Bas; Eduardo Conde-Sousa; Paulo Aguiar; Xiang Li; Yujie Li; Sumit Nanda; Yuan Wang; Leila Muresan; Pascal Fua; Bing Ye; Hai-yan He; Jochen F. Staiger; Manuel Peter; Daniel N. Cox; Michel Simonneau; Marcel Oberlaender; Gregory Jefferis; Kei Ito; Paloma Gonzalez-Bellido; Jinhyun Kim; Edwin Rubel; Hollis T. Cline; Hongkui Zeng; Aljoscha Nern; Ann-Shyn Chiang; Jianhua Yao; Jane Roskams; Rick Livesey; Janine Stevens; Tianming Liu; Chinh Dang; Yike Guo; Ning Zhong; Georgia Tourassi; Sean Hill; Michael Hawrylycz; Christof Koch; Erik Meijering; Giorgio A. Ascoli; Hanchuan Peng is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets about?

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t -distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary info

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Author

Linus Manubens-Gil; Zhi Zhou; Hanbo Chen; Arvind Ramanathan; Xiaoxiao Liu; Yufeng Liu; Alessandro Bria; Todd Gillette; Zongcai Ruan; Jian Yang; Miroslav Radojević; Ting Zhao; Li Cheng; Lei Qu; Siqi Liu; Kristofer E. Bouchard; Lin Gu; Weidong Cai; Shuiwang Ji; Badrinath Roysam; Ching-Wei Wang; Hongchuan Yu; Amos Sironi; Daniel Maxim Iascone; Jie Zhou; Erhan Bas; Eduardo Conde-Sousa; Paulo Aguiar; Xiang Li; Yujie Li; Sumit Nanda; Yuan Wang; Leila Muresan; Pascal Fua; Bing Ye; Hai-yan He; Jochen F. Staiger; Manuel Peter; Daniel N. Cox; Michel Simonneau; Marcel Oberlaender; Gregory Jefferis; Kei Ito; Paloma Gonzalez-Bellido; Jinhyun Kim; Edwin Rubel; Hollis T. Cline; Hongkui Zeng; Aljoscha Nern; Ann-Shyn Chiang; Jianhua Yao; Jane Roskams; Rick Livesey; Janine Stevens; Tianming Liu; Chinh Dang; Yike Guo; Ning Zhong; Georgia Tourassi; Sean Hill; Michael Hawrylycz; Christof Koch; Erik Meijering; Giorgio A. Ascoli; Hanchuan Peng

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)