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A spark of 3D revisualization: new method for re-exploring segmented data

Hu, Y.; Limaye, A.; Lu, J.

2020-08-03 evolutionary biology
10.1101/2020.08.01.222869 bioRxiv
Show abstract

3D scientific visualization is a popular non-destructive investigation tool, however current imaging processing and 3D visualization software has compatibility barriers which make replicability and reproducibility in research difficult. To solve this, we developed a new revisualization method and demonstrated four case studies using three mainstream image processing and 3D visualization software. Our method offers interchangeability amongst current image processing and 3D visualization software.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

1
Scientific Reports
○ 3612 papers in training set
Top 1%
15.3%
Similar papers in this journal
2
Nature Communications
○ 5641 papers in training set
Top 14%
12.8%
Similar papers in this journal
3
eLife
● 5828 papers in training set
Top 6%
12.8%
Similar papers in this journal
4
Communications Biology
○ 993 papers in training set
Top 0.2%
10.8%
Similar papers in this journal
50% of probability mass above
5
GigaScience
◐ 212 papers in training set
Top 0.5%
5.6%
Similar papers in this journal
6
Scientific Data
○ 209 papers in training set
Top 0.5%
5.2%
7
Nature Methods
○ 385 papers in training set
Top 3%
3.2%
8
iScience
○ 1154 papers in training set
Top 10%
2.5%
9
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 20%
2.5%
10
Advanced Biology
○ 29 papers in training set
Top 0.1%
2.4%
11
PLOS ONE
● 5266 papers in training set
Top 48%
1.5%
12
BMC Biology
○ 265 papers in training set
Top 3%
1.4%
13
Nature Protocols
○ 33 papers in training set
Top 0.4%
1.1%
14
Bioinformatics
◐ 1204 papers in training set
Top 8%
1.1%
15
Protein Science
○ 246 papers in training set
Top 3%
1.1%
16
Science Bulletin
○ 21 papers in training set
Top 0.3%
1.1%
17
Journal of Microscopy
○ 20 papers in training set
Top 0.2%
1.0%
18
Journal of Visualized Experiments
● 34 papers in training set
Top 0.5%
0.9%
19
Small Methods
○ 29 papers in training set
Top 0.7%
0.9%
20
The Analyst
● 16 papers in training set
Top 0.4%
0.9%
21
Advanced Intelligent Systems
○ 11 papers in training set
Top 0.2%
0.9%
22
Frontiers in Cell and Developmental Biology
○ 233 papers in training set
Top 6%
0.6%
23
Nature Biomedical Engineering
○ 47 papers in training set
Top 2%
0.6%
24
Development
● 497 papers in training set
Top 5%
0.6%
25
Molecular Biology of the Cell
● 311 papers in training set
Top 4%
0.6%
26
PLOS Biology
● 486 papers in training set
Top 13%
0.6%
27
Nature Biotechnology
○ 172 papers in training set
Top 5%
0.6%
28
eneuro
● 439 papers in training set
Top 8%
0.6%
29
Annals of Biomedical Engineering
○ 37 papers in training set
Top 1%
0.6%
30
Frontiers in Neuroinformatics
○ 41 papers in training set
Top 0.8%
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.