Author

Mengkuan Lin

Cold Spring Harbor Laboratory

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Neuroscience· 3

Signature Assays, Organisms & Methods

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Assay:imaging· 2 Organism:mouse· 2 Assay:spatial transcriptomics· 2 Research Focus:cell type annotation· 1 Research Focus:database / resource· 1 Approach:deep learning· 1 Research Focus:dimensionality reduction· 1 Approach:statistical model· 1

Frequent Co-Authors

Bing‐Xing HuoiD· 2 shared Katherine S. MathoiD· 2 shared Partha P. MitraiD· 2 shared Z. Josh HuangiD· 2 shared Alexander WoodwardiD· 1 shared Ali H. CetiniD· 1 shared Anan LiiD· 1 shared Anastasiia BludovaiD· 1 shared

Activity by Year

2020-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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2 shared papersNeuroscience

Bing‐Xing HuoORCID

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3 papers

Recent·Most cited

The Brain/MINDS 3D Digital Marmoset Brain Atlas Version 2.0: Population-Based Cortical Parcellations With Multi-Modal Standard Templates

Rui Gong, Noritaka Ichinohe, Hiroshi Abe, Toshiki Tani, Mengkuan Lin, Takuto Okuno, Ken Nakae, Junichi Hata, Shin Ishii, Patrice Jean Delmas, Shahrokh Heidari, Jiaxuan Wang, Tetsuo Yamamori, Hideyuki Okano, Alexander Woodward

The paper introduces BMA2.0, a population-based 3D marmoset brain atlas with multi-modal templates for improved cortical and subcortical parcellation and integration of multimodal data.

bioRxiv · Aug 28, 2025 · PreprintNeuroscience

Cellular Anatomy of the Mouse Primary Motor Cortex

Rodrigo Muñoz-Castañeda, Brian Zingg, Katherine S. Matho, Xiaoyin Chen, Quanxin Wang, Nicholas N. Foster, Anan Li, Arun Narasimhan, Karla E. Hirokawa, Bing‐Xing Huo, Samik Bannerjee, Laura Korobkova, Chris Sin Park, Young-Gyun Park, Michael S. Bienkowski, , Hong‐Wei Dong

The paper provides a detailed cellular-resolution map of the mouse primary motor cortex, defining cell types and their connectivity.

Nature · Oct 6, 2021Peer-reviewedNeuroscience

Semantic segmentation of microscopic neuroanatomical data by combining topological priors with encoder-decoder deep networks

Samik Banerjee, Lucas Magee, Dingkang Wang, Xu Li, Bing‐Xing Huo, Jaikishan Jayakumar, Katherine S. Matho, Mengkuan Lin, Keerthi Ram, Mohanasankar Sivaprakasam, Z. Josh Huang, Yusu Wang, Partha P. Mitra

The paper introduces a hybrid deep learning architecture combining topological priors with encoder-decoder networks to improve semantic segmentation of neuroanatomical data with high precision and recall.

Nature Machine Intelligence · Sep 28, 2020Peer-reviewedNeuroscience