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Topic · Biophysics

Cell Image Analysis Techniques

Cell Image Analysis Techniques is a research topic within Biophysics. Science Explorer counts 29k research works in it since 1950. 22.6% of them reached the world's top 10% most cited for their field and year.

This cluster of papers covers advanced techniques and tools in bioimage analysis, microscopy, and high-content screening. It includes topics such as machine learning for cellular image analysis, automated neuronal morphology reconstruction, deep learning applications, and phenotypic profiling of cellular responses. The papers also discuss the use of advanced image processing methods and the integration of high-throughput microscopy in drug discovery.

  • Bioimage Analysis
  • High-Content Screening
  • Microscopy
  • Machine Learning
  • Cellular Imaging
  • Neuronal Morphology
  • Automated Analysis
  • Deep Learning
  • Image Processing
  • Phenotypic Profiling
Research works
29k
fractional, since 1950
In the world top 10%
6.6k
per year above
Top-10% rate
22.6%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
+51%
the tick is no change

Which countries lead Cell Image Analysis Techniques research?

By volume, the United States and China publish the most (2.1k and 1.3k works in 2022–2025).

By volume, 2022–2025

  1. 1 United States 2.1k works
  2. 2 China 1.3k works
  3. 3 India 520 works
  4. 4 Germany 492 works
  5. 5 United Kingdom 381 works
  6. 6 France 265 works
  7. 7 Japan 255 works
  8. 8 Italy 217 works
  9. 9 Canada 173 works
  10. 10 South Korea 161 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

United States: 35.8%China: 22.2%India: 8.9%Germany: 8.4%6 others listed: 24.8%36%largest
United States2,098 · 35.8%China1,298 · 22.2%India520 · 8.9%Germany492 · 8.4%6 others listed1,452 · 24.8%

Shares of the rows listed above, not of the whole node.

Which institutions lead Cell Image Analysis Techniques research?

By volume in 2022–2025, University of North Carolina at Chapel Hill publishes the most Cell Image Analysis Techniques research, followed by Stanford University and Harvard University.

By volume, 2022–2025

  1. 1 University of North Carolina at Chapel Hill United States 37 works
  2. 2 Stanford University United States 36 works
  3. 3 Harvard University United States 34 works
  4. 4 Centre National de la Recherche Scientifique France 31 works
  5. 5 Tsinghua University China 29 works
  6. 6 Chinese Academy of Sciences China 28 works
  7. 7 University of Washington United States 26 works
  8. 8 Zhejiang University China 24 works
  9. 9 Johns Hopkins University United States 24 works
  10. 10 The University of Tokyo Japan 23 works

Where is Cell Image Analysis Techniques research done?

The largest centres of Cell Image Analysis Techniques research in 2022–2025 are Beijing (China), Shanghai (China), Paris (France) and London (United Kingdom). Among places with at least 20 works in it, it is an unusually large share of all research in Heidelberg and Cambridge.

Largest cities, 2022–2025

  1. 1 Beijing China 253 works
  2. 2 Shanghai China 121 works
  3. 3 Paris France 116 works
  4. 4 London United Kingdom 113 works
  5. 5 New York United States 97 works
  6. 6 Cambridge United States 89 works
  7. 7 Tokyo Japan 84 works
  8. 8 Seoul South Korea 77 works
  9. 9 Guangzhou China 71 works
  10. 10 Boston United States 66 works

Where it is the local speciality

  1. HeidelbergDE · 43.4 works5.7×
  2. CambridgeUS · 88.7 works4.7×
← less than its size predicts1×more →

Location quotient: how much more of its research is in Cell Image Analysis Techniques than the world average.

See Cell Image Analysis Techniques on the map

Where is the best place to study Cell Image Analysis Techniques?

Among universities, judged by research, Stanford University, École Polytechnique Fédérale de Lausanne and Harvard University score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%20%40%mean 35.9%fractional works in this node (log) →share in the world top 10% →Stanford University: 36, 32.8%École Polytechnique Fédérale de Lausanne: 17, 39.6%Harvard University: 34, 34.0%University of Hong Kong: 11, 38.6%University of Cambridge: 18, 47.0%University of California, Berkeley: 18, 37.6%Tsinghua University: 29, 37.7%Massachusetts Institute of Technology: 19, 35.6%University of Washington: 26, 31.8%Georgia Institute of Technology: 21, 24.3%École Polytechnique …University of Hong K…Harvard UniversityStanford University
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1Stanford University United States 53.432.8%4.1×36 +40.7%
2École Polytechnique Fédérale de Lausanne Switzerland 53.039.6%5.5×17 +10.5%
3Harvard University United States 52.134.0%3.6×34 +8.8%
4University of Hong Kong Hong Kong 50.538.6%1.7×11 +360.0%
5University of Cambridge United Kingdom 50.347.0%2.2×18 +52.5%
6University of California, Berkeley United States 48.337.6%2.9×18 +116.5%
7Tsinghua University China 47.137.7%2.0×29 +48.8%
8Massachusetts Institute of Technology United States 46.635.6%4.1×19 +27.6%
9University of Washington United States 45.531.8%3.5×26 +16.7%
10Georgia Institute of Technology United States 44.124.3%4.5×21 +78.9%

Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.

Is Cell Image Analysis Techniques research growing?

Output in 2018–2022 was 51% higher than in 2013–2017, peaking in 2025. The fastest-growing topics are Cell Image Analysis Techniques.

19801990200020102020
grewheldshrank

The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.