Science Explorer Back to mapMap
Discussion
Topic · Computer Vision and Pattern Recognition

Medical Image Segmentation Techniques

Medical Image Segmentation Techniques is a research topic within Computer Vision and Pattern Recognition. Science Explorer counts 43k research works in it since 1950. 9.9% of them reached the world's top 10% most cited for their field and year.

This cluster of papers covers advances in image segmentation techniques, particularly focusing on medical image analysis, graph cuts, active contours, MRI segmentation, deformable image registration, level set methods, statistical shape models, deep learning, and texture analysis.

  • Image Segmentation
  • Medical Image Analysis
  • Graph Cuts
  • Active Contours
  • MRI Segmentation
  • Deformable Image Registration
  • Level Set Methods
  • Statistical Shape Models
  • Deep Learning
  • Texture Analysis
Research works
43k
fractional, since 1950
In the world top 10%
4.3k
per year above
Top-10% rate
9.9%
share of its works in the world top 10%
Growth, 2018-21 → 2022-25
+35%
the tick is no change
Largest producer
China
52% of the 10 largest, 2022-now
Medical Image Segmentation Techniques

Which countries lead Medical Image Segmentation Techniques research?

By volume, China and India publish the most (3.4k and 966 works in 2022-now).

By volume, 2022-now

  1. 1 China 3.4k works
  2. 2 India 966 works
  3. 3 United States 900 works
  4. 4 United Kingdom 254 works
  5. 5 Germany 247 works
  6. 6 France 212 works
  7. 7 Japan 170 works
  8. 8 South Korea 165 works
  9. 9 Canada 136 works
  10. 10 Italy 109 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.

China: 51.6%India: 14.8%United States: 13.8%United Kingdom: 3.9%6 others listed: 15.9%52%largest
China3,364 · 51.6%India966 · 14.8%United States900 · 13.8%United Kingdom254 · 3.9%6 others listed1,039 · 15.9%

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

Which institutions lead Medical Image Segmentation Techniques research?

By volume in 2022-now, Chinese Academy of Sciences publishes the most Medical Image Segmentation Techniques research, followed by Shanghai Jiao Tong University and Centre National de la Recherche Scientifique.

By volume, 2022-now

  1. 1 Chinese Academy of SciencesChina 117 works
  2. 2 Shanghai Jiao Tong UniversityChina 72 works
  3. 3 Centre National de la Recherche ScientifiqueFrance 69 works
  4. 4 Fudan UniversityChina 42 works
  5. 5 University of Science and Technology of ChinaChina 41 works
  6. 6 Tsinghua UniversityChina 39 works
  7. 7 Sun Yat-sen UniversityChina 38 works
  8. 8 Sichuan UniversityChina 38 works
  9. 9 SRM Institute of Science and TechnologyIndia 38 works
  10. 10 University of Electronic Science and Technology of ChinaChina 38 works

Who are the leading researchers in Medical Image Segmentation Techniques?

The most-cited researchers publishing on Medical Image Segmentation Techniques include Yoshua Bengio, Ross Girshick and Jun Wang.

  1. 1 Yoshua Bengio Canada 476k citations
  2. 2 Ross Girshick Israel 356k citations
  3. 3 Jun Wang China 340k citations
  4. 4 Karl John Friston United Kingdom 310k citations
  5. 5 Dan J. Stein South Africa 306k citations
  6. 6 Trevor Hastie United States 301k citations
  7. 7 John Q. Trojanowski United States 286k citations
  8. 8 Luigi Ferrucci United States 281k citations
  9. 9 Xiaogang Wang China 260k citations
  10. 10 Yann LeCun United States 256k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

What are the most cited papers in Medical Image Segmentation Techniques?

The most cited paper in Medical Image Segmentation Techniques is "A Computational Approach to Edge Detection" (John Canny, 1986), with 29,563 citations. Next come "Textural Features for Image Classification" (22,878) and "Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain" (17,037).

Most cited papers
#PaperReleasedCitations
1 A Computational Approach to Edge Detection John Canny · IEEE Transactions on Pattern Analysis and Machine Intelligence Nov 1986 29,563
2 Textural Features for Image Classification Robert M. Haralick, K. Shanmugam, Its'Hak Dinstein · IEEE Transactions on Systems Man and Cybernetics Nov 1973 22,878
3 Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain N. Tzourio-Mazoyer, Brigitte Landeau, Dimitri Papathanassiou et al. · NeuroImage Open access 2002 17,037
4 Nonlinear total variation based noise removal algorithms L. Rudin, Stanley Osher, Emad Fatemi · Physica D Nonlinear Phenomena Nov 1992 15,969
5 Normalized cuts and image segmentation Jianbo Shi, Jitendra Malik · IEEE Transactions on Pattern Analysis and Machine Intelligence 2000 15,833
All 97 most cited papers, with filters and sorting

Where is Medical Image Segmentation Techniques research done?

The largest centres of Medical Image Segmentation Techniques research in 2022-now are Beijing (China), Shanghai (China), Xi'an (China) and Nanjing (China). Among places with at least 20 works in it, it is an unusually large share of all research in Shenzhen.

Largest cities, 2022-now

  1. 1 BeijingChina 479 works
  2. 2 ShanghaiChina 280 works
  3. 3 Xi'anChina 159 works
  4. 4 NanjingChina 155 works
  5. 5 WuhanChina 140 works
  6. 6 GuangzhouChina 133 works
  7. 7 ChengduChina 131 works
  8. 8 ShenzhenChina 130 works
  9. 9 ChennaiIndia 118 works
  10. 10 HangzhouChina 111 works

Where it is the local speciality

  1. ShenzhenCN · 130.3 works4.8×
← less than its size predicts1×more →

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

See Medical Image Segmentation Techniques on the map

Where is the best place to study Medical Image Segmentation Techniques?

Among universities, judged by research, Hong Kong University of Science and Technology, Chinese University of Hong Kong and Shanghai Jiao Tong University score highest, combining citation excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%10%20%30%mean 20.1%fractional works in this field (log) →share of work in the world top 10% →Hong Kong University…Sichuan UniversityChinese University o…Shanghai Jiao Tong U…
above the meannear itbelow it

One dot per university in the first ten of the table below, at the default weights. The upper left is the interesting corner: small places doing unusually strong work.

Citation excellenceshare of its work in the world top 10%, 2022-now and all time averaged
40%
Specialisationhow much of its work is in this field, counted up to 10× the world share
10%
Sizeits output in this field, on a log scale
30%
Growthoutput growth, counted from -20% to +20%
10%
Internationalshare of its work with a co-author abroad
10%
Each slider sets a weight; the share it gets of the score is beside it.
#UniversityScoreTop 10%SpecialisationWorksGrowth
1Hong Kong University of Science and TechnologyChina 74.528.1%6.2×18 +169.3%
2Chinese University of Hong KongChina 67.721.6%4.2×22 +61.1%
3Shanghai Jiao Tong UniversityChina 64.814.6%3.5×72 +85.1%
4Sichuan UniversityChina 64.322.2%2.4×38 +131.2%
5Hong Kong Polytechnic UniversityChina 64.324.2%2.5×15 +203.5%
6Harvard UniversityUnited States 61.716.7%1.4×32 +3.7%
7University of California, BerkeleyUnited States 60.520.5%1.7×11 +82.6%
8University of Hong KongChina 60.222.1%2.4×16 +121.3%
9University of Science and Technology of ChinaChina 60.114.8%4.8×40 +242.5%
10Fudan UniversityChina 59.516.2%3.8×42 +121.0%

Universities only. The score blends citation excellence (40%), size (30%), specialisation (10%), growth (10%), international reach (10%), 2022-now; growth compares 2018-21 with 2022-25 and counts up to +20%, since a jump from a small base is mostly noise.

Every institution in Medical Image Segmentation Techniques, country by country

Is Medical Image Segmentation Techniques research growing?

Output in 2022-2025 was 35% higher than in 2018-2021, peaking in 2025. The fastest-growing topics are Medical Image Segmentation Techniques.

still counting19801990200020102020
grewheldshrank

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

Its share of all research

Growing output can still be a shrinking share: all of science grows. This is Medical Image Segmentation Techniques as a percentage of every work published that year, 0.039% in 2024.

still counting0.050%0.10%0.15%Medical Image Segmentation Techniques0.041% in 202519801990200020102020

Fractional works of Medical Image Segmentation Techniques over all fractional works that year. How the balance of science has shifted.

How much of it reaches the world top 10%

Year by year.

still counting5%10%15%Medical Image Segmentation Techniques9.7% in 2025200020102020

Of each year's works in it, the share that reached the world's 10% most cited for their field and year. A field is the world for its own topics, so there is no other line to hold it against.

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.

Discussion

Ask a question or share a note about Medical Image Segmentation Techniques. Keep it to the research: no personal attacks, advertising or links. Any reader can report a post, which collapses it for everyone until it is opened.