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 www.scopus.com
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/j.media.2017.05.001
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/j.media.2017.11.005
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 www.scopus.com
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/j.media.2009.05.004
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 www.scopus.com
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 www.scopus.com
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 www.scopus.com
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 www.scopus.com
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 www.scopus.com
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 www.scopus.com
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 www.scopus.com
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 www.scopus.com
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/j.media.2014.10.012
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/j.media.2020.101693
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 www.scopus.com
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 www.scopus.com
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 www.scopus.com