TABLE 4.
TOP 49 annual citation rates publications and their categories. 1 (review), 2(1)(traditional segmentation algorithms), 2(2) (deep learning neural network based segmentation algorithms), and 3 (other relevant publications)
| Rank | Annual citation rates | Title | Category |
|---|---|---|---|
| 1 | 289.00 | “V‐Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation 48 ” | 2(2) |
| 2 | 68.75 | “UNet++ A Nested U‐Net Architecture for Medical Image Segmentation 52 ” | 2(2) |
| 3 | 67.92 | “Statistical shape models for 3D medical image segmentation: A review 15 ” | 1 |
| 4 | 66.32 | “Current methods in medical image segmentation 1 ” | 1 |
| 5 | 63.57 | “Metrics for evaluating 3D medical image segmentation: Analysis, selection, and tool 65 ” | 3 |
| 6 | 43.24 | “Interactive Medical Image Segmentation Using Deep Learning With Image‐Specific Fine Tuning 53 ” | 2(2) |
| 7 | 40.20 | “3D deeply supervised network for automated segmentation of volumetric medical images 51 ” | 2(2) |
| 8 | 37.33 | “CE‐Net: Context Encoder Network for 2D Medical Image Segmentation 59 ” | 2(2) |
| 9 | 36.00 | “Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges 31 ” | 1 |
| 10 | 35.46 | “Three‐dimensional multi‐scale line filter for segmentation and visualization of curvilinear structures in medical images 34 ” | 2(1) |
| 11 | 34.79 | “A Shape‐Based Approach to the Segmentation of Medical Imagery Using Level Sets 44 ” | 2(1) |
| 12 | 32.00 | “SegAN: Adversarial Network with Multi‐scale L1 Loss for Medical Image Segmentation 55 ” | 2(2) |
| 13 | 29.58 | “Automated medical image segmentation techniques 16 ” | 1 |
| 14 | 28.22 | “Improved Watershed Transform for Medical Image Segmentation Using Prior Information 36 ” | 2(1) |
| 15 | 27.82 | “Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation 45 ” | 2(1) |
| 16 | 23.75 | “Learning Normalized Inputs for Iterative Estimation in Medical Image Segmentation 50 ” | 2(2) |
| 17 | 23.61 | “A novel kernelized fuzzy C‐means algorithm with application in medical image segmentation 4 ” | 2(1) |
| 18 | 21.57 | “Medical image segmentation on GPUs – A comprehensive review 8 ” | 1 |
| 19 | 19.50 | “A review of algorithms for medical image segmentation and their applications to the female pelvic cavity 17 ” | 1 |
| 20 | 17.50 | “An application of cascaded 3D fully convolutional networks for medical image 58 ” | 2(2) |
| 21 | 17.11 | “Medical Image Segmentation by Combining Graph Cuts and Oriented Active Appearance Models 38 ” | 2(1) |
| 22 | 16.92 | “A Geometric Snake Model for Segmentation of Medical Imagery 33 ” | 2(1) |
| 23 | 16.88 | “Medical Image Segmentation Methods, Algorithms, and Applications 29 ” | 1 |
| 24 | 16.67 | “Active contour model based on local and global intensity information for medical image segmentation 9 ” | 2(1) |
| 25 | 15.80 | “Deep Learning for Multi‐task Medical Image Segmentation in Multiple Modalities 49 ” | 2(2) |
| 26 | 15.67 | “A novel segmentation model for medical images with intensity inhomogeneity based on adaptive perturbation 43 ” | 2(1) |
| 27 | 15.67 | “Data Augmentation Using Learned Transformations for One‐Shot Medical Image Segmentation 61 ” | 2(2) |
| 28 | 15.50 | “Convolutional neural network for bio‐medical image segmentation with hardware acceleration 67 ” | 3 |
| 29 | 15.29 | “A comparative study of deformable contour methods on medical image segmentation 30 ” | 1 |
| 30 | 15.00 | “Weighted Level Set Evolution Based on Local Edge Features for Medical Image Segmentation 47 ” | 2(1) |
| 31 | 14.00 | “DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation 54 ” | 2(2) |
| 32 | 13.75 | “Segmentation of Dental X‐ray Images in Medical Imaging using Neutrosophic Orthogonal Matrices 41 ” | 2(1) |
| 33 | 13.67 | “Aleatoric uncertainty estimation with test‐time augmentation for medical image segmentation with convolutional neural networks 68 ” | 3 |
| 34 | 13.67 | “NAS‐Unet: Neural Architecture Search for Medical Image Segmentation 60 ” | 2(2) |
| 35 | 13.19 | “Medical Image Segmentation Using K‐Means Clustering and Improved Watershed Algorithm 37 ” | 2(1) |
| 36 | 12.86 | “Dynamic‐context cooperative quantum‐behaved particle swarm optimization based on multilevel thresholding applied to medical image segmentation 40 ” | 2(1) |
| 37 | 12.22 | “Fast segmentation and high‐quality three‐dimensional volume mesh creation from medical images for diffuse optical tomography 39 ” | 2(1) |
| 38 | 12.00 | “Recurrent residual U‐Net for medical image segmentation 62 ” | 2(2) |
| 39 | 11.90 | “Interaction in the segmentation of medical images: A survey 28 ” | 1 |
| 40 | 11.75 | “DRINet for Medical Image Segmentation 56 ” | 2(2) |
| 41 | 11.71 | “Medical Image Segmentation Using New Hybrid Level‐Set Method 46 ” | 2(1) |
| 42 | 11.16 | “Deformable M‐Reps for 3D Medical Image Segmentation 35 ” | 2(1) |
| 43 | 11.00 | “ASDNet: Attention based semi‐supervised deep networks for medical image segmentation 57 ” | 2(2) |
| 44 | 11.00 | “High‐resolution encoder–decoder networks for low‐contrast medical image segmentation 63 ” | 2(2) |
| 45 | 10.85 | “Medical Image Segmentation Using Genetic Algorithms 19 ” | 1 |
| 46 | 10.50 | “Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation 32 ” | 1 |
| 47 | 10.20 | “A multi‐scale 3D Otsu thresholding algorithm for medical image segmentation 42 ” | 2(1) |
| 48 | 10.00 | “Accelerating compute intensive medical imaging segmentation algorithms using hybrid CPU‐GPU implementations 66 ” | 3 |
| 49 | 10.00 | “A software tool for automatic classification and segmentation of 2D/3D medical images 2 ” | 3 |