UPSI Digital Repository (UDRep)
Start | FAQ | About

QR Code Link :

Type :article
Subject :Z Bibliography. Library Science. Information Resources
ISSN :1526-9914
Main Author :Bahbibi Rahmatullah
Additional Authors :Bin, Zhang
Shir, Li Wang
Guangnan, Zhang
Huan, Wang
Ebrahim, Nader Ale
Title :A bibliometric of publication trends in medical image segmentation: quantitative and qualitative analysis
Place of Production :Tanjong Malim
Publisher :Fakulti Seni, Komputeran dan Industri Kreatif
Year of Publication :2021
Corporate Name :Universiti Pendidikan Sultan Idris
PDF Full Text :Login required to access this item.

Abstract : Universiti Pendidikan Sultan Idris
Purpose: Medical images are important in diagnosing disease and treatment planning. Computer algorithms that describe anatomical structures that highlight regions of interest and remove unnecessary information are collectively known as medical image segmentation algorithms. The quality of these algorithms will directly affect the performance of the following processing steps. There are many studies about the algorithms of medical image segmentation and their applications, but none involved a bibliometric of medical image segmentation. Methods: This bibliometric work investigated the academic publication trends in medical image segmentation technology. These data were collected from the Web of Science (WoS) Core Collection and the Scopus. In the quantitative analysis stage, important visual maps were produced to show publication trends from five different perspectives including annual publications, countries, top authors, publication sources, and keywords. In the qualitative analysis stage, the frequently used methods and research trends in the medical image segmentation field were analyzed from 49 publications with the top annual citation rates. Results: The analysis results showed that the number of publications had increased rapidly by year. The top related countries include the Chinese mainland, the United States, and India. Most of these publications were conference papers, besides there are also some top journals. The research hotspot in this field was deep learning-based medical image segmentation algorithms based on keyword analysis. These publications were divided into three categories: reviews, segmentation algorithm publications, and other relevant publications. Among these three categories, segmentation algorithm publications occupied the vast majority, and deep learning neural network-based algorithm was the research hotspots and frontiers. Conclusions: Through this bibliometric research work, the research hotspot in the medical image segmentation field is uncovered and can point to future research in the field. It can be expected that more researchers will focus their work on deep learning neural network-based medical image segmentation.


Aghaei Chadegani, A., Salehi, H., Md Yunus, M. M., Farhadi, H., Fooladi, M., Farhadi, M., & Ale Ebrahim, N. (2013). A comparison between two main academic literature collections: Web of science and scopus databases. Asian Social Science, 9(5), 18-26. doi:10.5539/ass.v9n5p18

Ale Ebrahim, N., Norfarah, N., Siti Nabiha, A. K., & Mohd Ali, S. (2019). Firms’ sustainable practice research in developing countries: Mapping the cited literature by bibliometric analysis approach. Int.J.Sustain.Strategic Manage., 7(1-2), 5-26. Retrieved from

Ali, M., Son, L. H., Khan, M., & Tung, N. T. (2018). Segmentation of dental X-ray images in medical imaging using neutrosophic orthogonal matrices. Expert Systems with Applications, 91, 434-441. doi:10.1016/j.eswa.2017.09.027

Alom, M. Z., Yakopcic, C., Hasan, M., Taha, T. M., & Asari, V. K. (2019). Recurrent residual U-net for medical image segmentation. Journal of Medical Imaging, 6(1) doi:10.1117/1.JMI.6.1.014006

Alsmirat, M. A., Jararweh, Y., Al-Ayyoub, M., Shehab, M. A., & Gupta, B. B. (2017). Accelerating compute intensive medical imaging segmentation algorithms using hybrid CPU-GPU implementations. Multimedia Tools and Applications, 76(3), 3537-3555. doi:10.1007/s11042-016-3884-2

Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. doi:10.1016/j.joi.2017.08.007

Bezdek, J. C., Hall, L. O., & Clarke, L. P. (1993). Review of MR image segmentation techniques using pattern recognition. Medical Physics, 20(4), 1033-1048. doi:10.1118/1.597000

Chen, L., Bentley, P., Mori, K., Misawa, K., Fujiwara, M., & Rueckert, D. (2018). DRINet for medical image segmentation. IEEE Transactions on Medical Imaging, 37(11), 2453-2462. doi:10.1109/TMI.2018.2835303

Chen, X., Udupa, J. K., Bagci, U., Zhuge, Y., & Yao, J. (2012). Medical image segmentation by combining graph cuts and oriented active appearance models. IEEE Transactions on Image Processing, 21(4), 2035-2046. doi:10.1109/TIP.2012.2186306

Connelly, T. M., Malik, Z., Sehgal, R., Byrnes, G., Coffey, J. C., & Peirce, C. (2020). The 100 most influential manuscripts in robotic surgery: A bibliometric analysis. Journal of Robotic Surgery, 14(1), 155-165. doi:10.1007/s11701-019-00956-9

Dou, Q., Yu, L., Chen, H., Jin, Y., Yang, X., Qin, J., & Heng, P. -. (2017). 3D deeply supervised network for automated segmentation of volumetric medical images. Medical Image Analysis, 41, 40-54. doi:10.1016/

Drozdzal, M., Chartrand, G., Vorontsov, E., Shakeri, M., Di Jorio, L., Tang, A., . . . Kadoury, S. (2018). Learning normalized inputs for iterative estimation in medical image segmentation. Medical Image Analysis, 44, 1-13. doi:10.1016/

Ebrahim, S. A., Pedram, M. Z., & Ebrahim, N. A. (2020). Current status of systemic drug delivery research: A bibliometric study. Systemic Delivery Technologies in Anti-Aging Medicine: Methods and Applications, , 39-55. Retrieved from

Feng, Y., Zhao, H., Li, X., Zhang, X., & Li, H. (2017). A multi-scale 3D otsu thresholding algorithm for medical image segmentation. Digital Signal Processing: A Review Journal, 60, 186-199. doi:10.1016/j.dsp.2016.08.003

Grau, V., Mewes, A. U. J., Alcañiz, M., Kikinis, R., & Warfield, S. K. (2004). Improved watershed transform for medical image segmentation using prior information. IEEE Transactions on Medical Imaging, 23(4), 447-458. doi:10.1109/TMI.2004.824224

Gu, W., Yuan, Y., Yang, H., Qi, G., Jin, X., & Yan, J. (2015). A bibliometric analysis of the 100 most influential papers on COPD. International Journal of COPD, 10, 667-676. doi:10.2147/COPD.S74911

Gu, Z., Cheng, J., Fu, H., Zhou, K., Hao, H., Zhao, Y., . . . Liu, J. (2019). CE-net: Context encoder network for 2D medical image segmentation. IEEE Transactions on Medical Imaging, 38(10), 2281-2292. doi:10.1109/TMI.2019.2903562

He, L., Fang, H., Wang, X., Wang, Y., Ge, H., Li, C., . . . He, H. (2020). The 100 most-cited articles in urological surgery: A bibliometric analysis. International Journal of Surgery, 75, 74-79. doi:10.1016/j.ijsu.2019.12.030

He, L., Peng, Z., Everding, B., Wang, X., Han, C. Y., Weiss, K. L., & Wee, W. G. (2008). A comparative study of deformable contour methods on medical image segmentation. Image and Vision Computing, 26(2), 141-163. doi:10.1016/j.imavis.2007.07.010

Heimann, T., & Meinzer, H. -. (2009). Statistical shape models for 3D medical image segmentation: A review. Medical Image Analysis, 13(4), 543-563. doi:10.1016/

Hesamian, M. H., Jia, W., He, X., & Kennedy, P. (2019). Deep learning techniques for medical image segmentation: Achievements and challenges. Journal of Digital Imaging, 32(4), 582-596. doi:10.1007/s10278-019-00227-x

Hu, Y., Yu, Z., Chen, X., Luo, Y., & Wen, C. (2020). A bibliometric analysis and visualization of medical data mining research. Medicine, 99(22), e20338. doi:10.1097/MD.0000000000020338

Jermyn, M., Ghadyani, H., Mastanduno, M. A., Turner, W., Davis, S. C., Dehghani, H., & Pogue, B. W. (2013). Fast segmentation and high-quality three-dimensional volume mesh creation from medical images for diffuse optical tomography. Journal of Biomedical Optics, 18(8) doi:10.1117/1.JBO.18.8.086007

Khadidos, A., Sanchez, V., & Li, C. -. (2017). Weighted level set evolution based on local edge features for medical image segmentation. IEEE Transactions on Image Processing, 26(4), 1979-1991. doi:10.1109/TIP.2017.2666042

Li, B. N., Chui, C. K., Chang, S., & Ong, S. H. (2011). Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation. Computers in Biology and Medicine, 41(1), 1-10. doi:10.1016/j.compbiomed.2010.10.007

Li, D. (2018). Transforming Time Series for Efficient and Accurate Classification, Retrieved from

Li, Y., Jiao, L., Shang, R., & Stolkin, R. (2015). Dynamic-context cooperative quantum-behaved particle swarm optimization based on multilevel thresholding applied to medical image segmentation. Information Sciences, 294, 408-422. doi:10.1016/j.ins.2014.10.005

Ma, Z., Tavares, J. M. R. S., Jorge, R. N., & Mascarenhas, T. (2010). A review of algorithms for medical image segmentation and their applications to the female pelvic cavity. Computer Methods in Biomechanics and Biomedical Engineering, 13(2), 235-246. doi:10.1080/10255840903131878

Maghami, M. R., Asl, S. N., Rezadad, M. E., Ale Ebrahim, N., & Gomes, C. (2015). Qualitative and quantitative analysis of solar hydrogen generation literature from 2001 to 2014. Scientometrics, 105(2), 759-771. doi:10.1007/s11192-015-1730-3

Maulik, U. (2009). Medical image segmentation using genetic algorithms. IEEE Transactions on Information Technology in Biomedicine, 13(2), 166-173. doi:10.1109/TITB.2008.2007301

Milletari, F., Navab, N., & Ahmadi, S. -. (2016). V-net: Fully convolutional neural networks for volumetric medical image segmentation. Paper presented at the Proceedings - 2016 4th International Conference on 3D Vision, 3DV 2016, 565-571. doi:10.1109/3DV.2016.79 Retrieved from

Moeskops, P., Wolterink, J. M., van der Velden, B. H. M., Gilhuijs, K. G. A., Leiner, T., Viergever, M. A., & Išgum, I. (2016). Deep learning for multi-task medical image segmentation in multiple modalities doi:10.1007/978-3-319-46723-8_55 Retrieved from

Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., Altman, D., Antes, G., . . . Tugwell, P. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine, 6(7) doi:10.1371/journal.pmed.1000097

Ng, H. P., Ong, S. H., Foong, K. W. C., Goh, P. S., & Nowinski, W. L. (2006). Medical image segmentation using k-means clustering and improved watershed algorithm. Paper presented at the Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation, , 2006 61-65. Retrieved from

Nie, D., Gao, Y., Wang, L., & Shen, D. (2018). ASDNet: Attention based semi-supervised deep networks for medical image segmentation doi:10.1007/978-3-030-00937-3_43 Retrieved from

Norouzi, A., Rahim, M. S. M., Altameem, A., Saba, T., Rad, A. E., Rehman, A., & Uddin, M. (2014). Medical image segmentation methods, algorithms, and applications. IETE Technical Review (Institution of Electronics and Telecommunication Engineers, India), 31(3), 199-213. doi:10.1080/02564602.2014.906861

Olabarriaga, S. D., & Smeulders, A. W. M. (2001). Interaction in the segmentation of medical images: A survey. Medical Image Analysis, 5(2), 127-142. doi:10.1016/S1361-8415(00)00041-4

Pham, D. L., Xu, C., & Prince, J. L. (2000). Current methods in medical image segmentation doi:10.1146/annurev.bioeng.2.1.315 Retrieved from

Pizer, S. M., Fletcher, P. T., Joshi, S., Thall, A., Chen, J. Z., Fridman, Y., . . . Chaney, E. L. (2003). Deformable M-reps for 3D medical image segmentation. International Journal of Computer Vision, 55(2-3), 85-106. doi:10.1023/A:1026313132218

Rahmatullah, B., & Besar, R. (2009). Analysis of semi-automated method for femur length measurement from foetal ultrasound. Journal of Medical Engineering and Technology, 33(6), 417-425. doi:10.1080/03091900802451232

Rahmatullah, B., & Noble, J. A. (2014). Anatomical object detection in fetal ultrasound: Computer-expert agreements doi:10.1007/978-3-642-54121-6_18 Retrieved from

Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation doi:10.1007/978-3-319-24574-4_28 Retrieved from

Roth, H. R., Oda, H., Zhou, X., Shimizu, N., Yang, Y., Hayashi, Y., . . . Mori, K. (2018). An application of cascaded 3D fully convolutional networks for medical image segmentation. Computerized Medical Imaging and Graphics, 66, 90-99. doi:10.1016/j.compmedimag.2018.03.001

Rueda, S., Fathima, S., Knight, C. L., Yaqub, M., Papageorghiou, A. T., Rahmatullah, B., . . . Noble, J. A. (2014). Evaluation and comparison of current fetal ultrasound image segmentation methods for biometric measurements: A grand challenge. IEEE Transactions on Medical Imaging, 33(4), 797-813. doi:10.1109/TMI.

Sato, Y., Nakajima, S., Shiraga, N., Atsumi, H., Yoshida, S., Koller, T., . . . Kikinis, R. (1998). Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. Medical Image Analysis, 2(2), 143-168. doi:10.1016/S1361-8415(98)80009-1

Sharma, N., Ray, A. K., Shukla, K. K., Sharma, S., Pradhan, S., Srivastva, A., & Aggarwal, L. (2010). Automated medical image segmentation techniques. Journal of Medical Physics, 35(1), 3-14. doi:10.4103/0971-6203.58777

Smistad, E., Falch, T. L., Bozorgi, M., Elster, A. C., & Lindseth, F. (2015). Medical image segmentation on GPUs - A comprehensive review. Medical Image Analysis, 20(1), 1-18. doi:10.1016/

Strzelecki, M., Szczypinski, P., Materka, A., & Klepaczko, A. (2013). A software tool for automatic classification and segmentation of 2D/3D medical images. Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 702, 137-140. doi:10.1016/j.nima.2012.09.006

Taha, A. A., & Hanbury, A. (2015). Metrics for evaluating 3D medical image segmentation: Analysis, selection, and tool. BMC Medical Imaging, 15(1) doi:10.1186/s12880-015-0068-x

Tajbakhsh, N., Jeyaseelan, L., Li, Q., Chiang, J. N., Wu, Z., & Ding, X. (2020). Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation. Medical Image Analysis, 63 doi:10.1016/

Tsai, A., Yezzi Jr., A., Wells, W., Tempany, C., Tucker, D., Fan, A., . . . Willsky, A. (2003). A shape-based approach to the segmentation of medical imagery using level sets. IEEE Transactions on Medical Imaging, 22(2), 137-154. doi:10.1109/TMI.2002.808355

Vardhana, M., Arunkumar, N., Lasrado, S., Abdulhay, E., & Ramirez-Gonzalez, G. (2018). Convolutional neural network for bio-medical image segmentation with hardware acceleration. Cognitive Systems Research, 50, 10-14. doi:10.1016/j.cogsys.2018.03.005

Wang, G., Li, W., Aertsen, M., Deprest, J., Ourselin, S., & Vercauteren, T. (2019). Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks. Neurocomputing, 338, 34-45. doi:10.1016/j.neucom.2019.01.103

Wang, G., Li, W., Zuluaga, M. A., Pratt, R., Patel, P. A., Aertsen, M., . . . Vercauteren, T. (2018). Interactive medical image segmentation using deep learning with image-specific fine tuning. IEEE Transactions on Medical Imaging, 37(7), 1562-1573. doi:10.1109/TMI.2018.2791721

Wang, G., Zuluaga, M. A., Li, W., Pratt, R., Patel, P. A., Aertsen, M., . . . Vercauteren, T. (2019). DeepIGeoS: A deep interactive geodesic framework for medical image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(7), 1559-1572. doi:10.1109/TPAMI.2018.2840695

Wang, Z., Zhang, T., Huang, F., & Wang, Z. (2018). The reproductive and developmental toxicity of nanoparticles: A bibliometric analysis. Toxicology and Industrial Health, 34(3), 169-177. doi:10.1177/0748233717744430

Wells Iii, W. M., Crimson, W. E. L., Kikinis, R., & Jolesz, F. A. (1996). Adaptive segmentation of mri data. IEEE Transactions on Medical Imaging, 15(4), 429-442. doi:10.1109/42.511747

Weng, Y., Zhou, T., Li, Y., & Qiu, X. (2019). NAS-unet: Neural architecture search for medical image segmentation. IEEE Access, 7, 44247-44257. doi:10.1109/ACCESS.2019.2908991

Xue, Y., Xu, T., Zhang, H., Long, L. R., & Huang, X. (2018). SegAN: Adversarial network with multi-scale L 1 loss for medical image segmentation. Neuroinformatics, 16(3-4), 383-392. doi:10.1007/s12021-018-9377-x

Yan, Z., Matuszewski, B. J., Shark, L. -., & Moore, C. J. (2008). Medical image segmentation using new hybrid level-set method. Paper presented at the Proceedings - 5th International Conference BioMedical Visualization, Information Visualization in Medical and Biomedical Informatics, MediVis 2008, 71-76. doi:10.1109/MediVis.2008.12 Retrieved from

Yezzi Jr., A., Kichenassamy, S., Kumar, A., Olver, P., & Tannenbaum, A. (1997). A geometric snake model for segmentation of medical imagery. IEEE Transactions on Medical Imaging, 16(2), 199-209. doi:10.1109/42.563665

Yu, H., He, F., & Pan, Y. (2019). A novel segmentation model for medical images with intensity inhomogeneity based on adaptive perturbation. Multimedia Tools and Applications, 78(9), 11779-11798. doi:10.1007/s11042-018-6735-5

Zhang, B., Rahmatullah, B., Wang, S. L., Zaidan, A. A., Zaidan, B. B., & Liu, P. (2020). A review of research on medical image confidentiality related technology coherent taxonomy, motivations, open challenges and recommendations. Multimedia Tools and Applications, doi:10.1007/s11042-020-09629-4

Zhang, D. -., & Chen, S. -. (2004). A novel kernelized fuzzy C-means algorithm with application in medical image segmentation. Artificial Intelligence in Medicine, 32(1), 37-50. doi:10.1016/j.artmed.2004.01.012

Zhao, A., Balakrishnan, G., Durand, F., Guttag, J. V., & Dalca, A. V. (2019). Data augmentation using learned transforms for one-shot medical image segmentation. Data Augmentation using Learned Transforms for One-Shot Medical Image Segmentation, , 8543-8553. Retrieved from

Zhou, S., Nie, D., Adeli, E., Yin, J., Lian, J., & Shen, D. (2020). High-resolution encoder-decoder networks for low-contrast medical image segmentation. IEEE Transactions on Image Processing, 29, 461-475. doi:10.1109/TIP.2019.2919937

Zhou, S., Wang, J., Zhang, S., Liang, Y., & Gong, Y. (2016). Active contour model based on local and global intensity information for medical image segmentation. Neurocomputing, 186, 107-118. doi:10.1016/j.neucom.2015.12.073

Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., & Liang, J. (2018). Unet++: A nested u-net architecture for medical image segmentation. UNet++: A Nested U-Net Architecture for Medical Image Segmentation, , 3-11. Retrieved from

This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials.
You may use the digitized material for private study, scholarship, or research.

Back to previous page

Installed and configured by Bahagian Automasi, Perpustakaan Tuanku Bainun, Universiti Pendidikan Sultan Idris
If you have enquiries with this repository, kindly contact us at or Whatsapp +60163630263 (Office hours only)