A neuronal imaging dataset for deep learning in the reconstruction of single-neuron axons
Neuron reconstruction is a critical step in quantifying neuronal structures from imaging data. Advances in molecular labeling techniques and optical imaging technologies have spurred extensive research into the patterns of long-range neuronal projections. However, mapping these projections incurs si...
Saved in:
| Main Authors: | Liya Li, Ying Hu, Xiaojun Wang, Pei Sun, Tingwei Quan |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-08-01
|
| Series: | Frontiers in Neuroinformatics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fninf.2025.1628030/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Unravelling axonal transcriptional landscapes: insights from induced pluripotent stem cell-derived cortical neurons and implications for motor neuron degeneration
by: Jishu Xu, et al.
Published: (2025-06-01) -
KIF5A regulates axonal repair and time-dependent axonal transport of SFPQ granules and mitochondria in human motor neurons
by: Irune Guerra San Juan, et al.
Published: (2025-01-01) -
Neuron identity switches in response to the gradient gene expression pathway
by: Gustavo Guzmán, et al.
Published: (2025-02-01) -
Imp and Chinmo are required for embryonic motor neuron axon and dendrite targeting
by: Katherine H. Fisher, et al.
Published: (2025-07-01) -
Neuronal guidance behaviours: the primary cilium perspective
by: Melody Atkins, et al.
Published: (2025-06-01)