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. 17.4% 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%
- 7.5k per year above
- Top-10% rate
- 17.4% share of its works in the world top 10%
- Growth, 2013–17 → 2018–22
- -8% the tick is no change
Which countries lead Medical Image Segmentation Techniques research?
By volume, China and India publish the most (2.8k and 824 works in 2022–2025).
By volume, 2022–2025
- 1 China 2.8k works
- 2 India 824 works
- 3 United States 787 works
- 4 United Kingdom 219 works
- 5 Germany 215 works
- 6 France 191 works
- 7 Japan 145 works
- 8 South Korea 141 works
- 9 Canada 119 works
- 10 Italy 92 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.
Shares of the rows listed above, not of the whole node.
Which institutions lead Medical Image Segmentation Techniques research?
By volume in 2022–2025, Shanghai Jiao Tong University publishes the most Medical Image Segmentation Techniques research, followed by Chinese Academy of Sciences and University of Electronic Science and Technology of China.
By volume, 2022–2025
- 1 Shanghai Jiao Tong UniversityChina 51 works
- 2 Chinese Academy of SciencesChina 38 works
- 3 University of Electronic Science and Technology of ChinaChina 35 works
- 4 University of Science and Technology of ChinaChina 35 works
- 5 Harbin Institute of TechnologyChina 32 works
- 6 Shenzhen UniversityChina 31 works
- 7 Vellore Institute of Technology UniversityIndia 30 works
- 8 Beijing Institute of TechnologyChina 30 works
- 9 Tsinghua UniversityChina 29 works
- 10 Huazhong University of Science and TechnologyChina 28 works
Who are the leading researchers in Medical Image Segmentation Techniques?
The most-cited researchers publishing on Medical Image Segmentation Techniques include Andrew Zisserman, Serge Belongie and Jitendra Malik.
- 1 Andrew Zisserman United Kingdom 25k citations
- 2 Serge Belongie United States 14k citations
- 3 Jitendra Malik United States 12k citations
- 4 Pietro Perona United States 11k citations
- 5 Michael Maire United States 10k citations
- 6 Luc Van Gool Switzerland 8.5k citations
- 7 Alan Yuille United States 8.4k citations
- 8 Stanley Osher United States 8k citations
- 9 Jiaya Jia Hong Kong 7.5k citations
- 10 Thomas S. Huang United States 7.4k citations
Ranked by citations received across their whole record, among researchers with at least three works on this topic.
Where is Medical Image Segmentation Techniques research done?
The largest centres of Medical Image Segmentation Techniques research in 2022–2025 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, 2022–2025
Where it is the local speciality
- ShenzhenCN · 117.2 works5.0×
Location quotient: how much more of its research is in Medical Image Segmentation Techniques than the world average.
Where is the best place to study Medical Image Segmentation Techniques?
Among universities, judged by research, Hong Kong University of Science and Technology, ShanghaiTech University and Chongqing University of Posts and Telecommunications 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.
One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.
| # | University | Score | Top 10% | Specialisation | Works | Growth |
|---|---|---|---|---|---|---|
| 1 | Hong Kong University of Science and TechnologyHong Kong | 62.4 | 46.4% | 5.9× | 16 | -7.7% |
| 2 | ShanghaiTech UniversityChina | 55.1 | 29.3% | 9.4× | 12 | — |
| 3 | Chongqing University of Posts and TelecommunicationsChina | 54.2 | 24.7% | 9.5× | 19 | +28.4% |
| 4 | Vellore Institute of Technology UniversityIndia | 51.2 | 18.1% | 4.4× | 30 | +219.7% |
| 5 | Chinese University of Hong KongHong Kong | 50.1 | 34.1% | 4.1× | 20 | -25.3% |
| 6 | Shenzhen UniversityChina | 48.4 | 21.5% | 6.1× | 31 | -2.9% |
| 7 | Shandong Normal UniversityChina | 46.6 | 30.6% | 4.9× | 8 | +219.3% |
| 8 | Shanghai Jiao Tong UniversityChina | 46.0 | 25.1% | 3.4× | 51 | -29.0% |
| 9 | Xi’an University of Posts and TelecommunicationsChina | 44.2 | 9.9% | 12.8× | 18 | +21.8% |
| 10 | Soochow UniversityChina | 44.0 | 27.7% | 3.1× | 17 | +129.2% |
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 Medical Image Segmentation Techniques research growing?
Output in 2018–2022 was 8% lower than in 2013–2017, peaking in 2025. The fastest-growing topics are Medical Image Segmentation Techniques.
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.