Back

NeuroFlow: An Integrated, Cross-Platform Workflow for Mouse Brain Atlas Registration and Quantification

Rao, A.; Oo, H. Z.; Tao, C.; Zhang, G.-W.

2026-07-20 neuroscience
10.64898/2026.07.15.737186 bioRxiv
Show abstract

Registration of histological sections to a reference atlas is essential for anatomical localization and region-based quantitative analysis. Although established workflows are powerful, image preparation, registration, quantification, and visualization often rely on multiple software packages, some of which require platform-specific installation or locally configured programming environments. Here, we present NeuroFlow, a browser-based workflow for quantitative analysis of mouse brain histology. NeuroFlow integrates image registration, signal detection, quantification, and visualization within a single interface and operates across major operating systems without additional software installation. It supports affine and nonlinear alignment, as well as real-time oblique reslicing of the reference atlas. All processing is performed locally in a desktop browser, without requiring a local Python environment, MATLAB installation, or associated packages and toolboxes, and without uploading images to a remote server. This design preserves user control over data and keeps intermediate results accessible for inspection and review. NeuroFlow is available at https://guangweizhang.com/tool-neuroflow.html.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
Nature Methods
○ 385 papers in training set
Top 0.6%
15.0%
Similar papers in this journal
2
Journal of Neuroscience Methods
○ 122 papers in training set
Top 0.1%
10.6%
Similar papers in this journal
3
eneuro
● 439 papers in training set
Top 0.3%
9.7%
Similar papers in this journal
4
eLife
● 5828 papers in training set
Top 20%
6.2%
Similar papers in this journal
5
Nature Protocols
○ 33 papers in training set
Top 0.1%
4.8%
6
Scientific Data
○ 209 papers in training set
Top 0.5%
4.8%
Similar papers in this journal
50% of probability mass above
7
Journal of Visualized Experiments
● 34 papers in training set
Top 0.1%
4.3%
8
PLOS ONE
● 5266 papers in training set
Top 33%
4.0%
9
Frontiers in Neuroinformatics
○ 41 papers in training set
Top 0.2%
3.4%
10
Nature Communications
○ 5641 papers in training set
Top 35%
3.2%
11
Neuroinformatics
○ 46 papers in training set
Top 0.2%
3.1%
12
PLOS Biology
● 486 papers in training set
Top 3%
2.4%
13
Scientific Reports
○ 3612 papers in training set
Top 42%
2.4%
14
Aperture Neuro
● 20 papers in training set
Top 0.2%
1.7%
15
Cell Reports Methods
○ 165 papers in training set
Top 2%
1.5%
16
NeuroImage
○ 903 papers in training set
Top 5%
1.4%
17
Nature Neuroscience
○ 252 papers in training set
Top 4%
1.3%
18
Development
● 497 papers in training set
Top 3%
1.3%
19
Frontiers in Neuroscience
○ 256 papers in training set
Top 5%
1.1%
20
BMC Bioinformatics
○ 457 papers in training set
Top 5%
1.1%
21
Nature
○ 645 papers in training set
Top 9%
1.0%
22
PLOS Computational Biology
● 1863 papers in training set
Top 18%
1.0%
23
Journal of Open Source Software
● 25 papers in training set
Top 0.3%
1.0%
24
Imaging Neuroscience
● 282 papers in training set
Top 4%
0.8%
25
Neurophotonics
● 42 papers in training set
Top 0.6%
0.8%
26
STAR Protocols
○ 18 papers in training set
Top 0.3%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.