Table 2.
Study descriptors including the anatomical site, imaging modality, segmentation approach and evaluation matrix
| Study | Subjects | Part | Training and evaluation data | Image Modality | Segmentation approach | Evaluation metrics | Results |
|---|---|---|---|---|---|---|---|
| Chen, Wang, et al. [4] | 30 subjects, 20 females | maxilla | 30 scans for training, 6 for testing | CBCT | ML, Learning-based multi-source IntegratioN frameworK for Segmentation (LINKS) | DSC | The average Dice ratio of the maxilla was 0.800 ± 0.029. |
| Chen, Du, et al. [27] | 25 subjects, 11 females, age range of 10 to 49 years | teeth | 20 scans for training, 5 for testing | CBCT | DL, multi-task 3D fully convolutional network (FCN), and marker-controlled watershed transform (MWT) | DSC, Jaccard index | The average Dice similarity coefficient, and Jaccard index were 0.936 (± 0.012) and 0.881 (± 0.019). |
| Chung et al. [28] | NR | teeth | 150 scans for training, 25 for testing | CBCT | DL, single CNN, base architecture of the 3D U-net | Aggregate Jaccard index (AJI), ASSD | AJI score of 0.86 ± 0.01, and ASSD [mm] of 0.20 ± 0.10 |
| Cui et al. [29] | 4,215 subjects, 2312 females, mean age of 38.4 years | teeth, alveolar bone | 4531 images for training, 407 for testing | CBCT | DL | Dice ratio, ASD |
Average Dice scores of 94.1%, ASD (mm) of 0.17 for tooth. Average Dice scores of 94.1 (76.9–96.9) %, ASD (mm) of 0.35 (0.18–0.84) for maxillary bone. Average Dice scores of 94.8 (80.3–97.3) %, ASD (mm) of 0.29 (0.13–0.77) for mandible bone. |
| Duan et al. [30] | NR | teeth | 20 sets of images for training and testing | CBCT | DL, Region Proposal Network (RPN) with Feature Pyramid Network (FPN) method | DSC, ASD, RVD |
They achieved an average dice of 95.7% (0.957 ± 0.005) for single-rooted tooth ST, 96.2% (0.962 ± 0.002) for multirooted tooth MT. The ASD was 0.104 ± 0.019 for ST and 0.137 ± 0.019 for MT. The RVD was 0.049 ± 0.017 for ST and 0.053 ± 0.010 for MT. |
| Hsu et al. [31] | 24 subjects, 9 females, mean age of 29.1 ± 14.7 years | teeth | 24 sets of images for training and testing | CBCT | DL, 3.5D U-Net | DSC | The 3.5 Dv5 U-Net achieved highest DSC among all U-Nets. |
| Ileșan et al. [32] | NR | mandible | 120 scans for training, 40 for validation | CBCT and CT | DL, CNN, a 3D U-Net | DSC | Mean DSC was 0.884 for teeth segmentation and 0.894 for mandible segmentation |
| Jaskari et al. [33] | 594 subjects | mandibular canal | 457 scans for training, 52 for validation, 128 for testing | CBCT | DL, fully CNN | DSC, MCD |
DSC was 0.57 for the left canal and 0.58 for the right canal, MCD was 0.61 mm for the left canal and 0.50 mm for the right canal. |
| Kim et al. [34] | 25 subjects | mandibular condyle | 18 subject cases for training, 5 for validation, 2 for training | CBCT | DL, modified U-Net, and a CNN | IoU, HD |
Intersection over union (IoU) was 0.870 ± 0.023 for marrow bone and 0.734 ± 0.032 for cortical bone. The Hausdorff distance was 0.928 ± 0.166 mm for marrow bone and 1.247 ± 0.430 mm for cortical bone. |
| Kwak et al. [5] | 102 subjects, age range of 18–90 years | mandibular canal | 29, 456 images for training, 9818 for validation, 9818 for testing | CBCT | DL, deep CNN | Global classification accuracy | Global accuracy of 0.99 using a 3D U-Net CNN model for segmentation of mandibular canal |
| Lahoud et al. [35] | 46 subjects | teeth | 2095 samples for training, 501 for optimization, 328 for validation | CBCT | DL, deep CNN | IoU, | The mean intersection over union IOU for full-tooth segmentation was 0.87 (60.03) and 0.88 (60.03) for semiautomated (SA) |
| Lee et al. [24] | 102 subjects | teeth | 1066 images for training, 400 for validation, 151 for testing | CBCT | DL, CNN | Dice ratio | Dice value of 0.918. |
| Lin et al. [36] | 220 subjects, 136 females, mean age of 36.93 ± 13.77 years | mandibular canal | 132 images for training, 44 for validation, 44 for testing | CBCT | DL, CNN, two-stage 3D-Unet | DSC, 95% HD | The mean DSC was 0.875 ± 0.045 and the mean 95% HD was 0.442 ± 0.379. |
| Lo Giudice et al. [37] | 40 subjects, 20 females, mean age of 23.37 ± 3.34 years | mandible | 20 scans for training, 20 for testing | CBCT | DL, CNN | DSC, surface-to-surface matching | DSC of 0.972. |
| Macho et al. [38] | 40 subjects | teeth | 36 images for training, 4 for validation | CT | DL, CNN employing 3D volumetric convolutions | dice ratio | Dice value between 0.88 and 0.94. |
| Miki et al. [39] | 52 subjects | teeth | 42 subject cases for training, 10 for testing | CBCT | DL, deep CNN | classification accuracy | Classification accuracy 88.8% |
| Minnema et al. [20] | NR | teeth | 20 scans for training,2 for validation, 18 for testing | CBCT | DL, mixed-scale dense (MS-D) CNN | DSC | DSC of 0.87 ± 0.06 |
| Pankert et al. [40] | 307 subjects, 112 females, mean age of 63 years | mandible | 248 images for testing, 30 for validation, 29 for testing | CT | DL, two-stepped CNN | DSC, ASD, HD | Dice coefficient of 94.824% and an average surface distance of 0.31 mm. |
| Park et al. [41] | 171 subjects | mandible, maxilla | 146 sets for training, 10 for validation, 15 for testing | CT | DL, hierarchical, parallel, and multi-scale residual block to the U-Net (HPMR-U-Net) | DSC, ASD, 95HD | DC of 90.2 ± 19.5 for maxilla segmentation and 97.4 ± 0.4 for mandible segmentation |
| Qiu et al. [6] | 11 subjects | mandible | 8 scans for training, 2 for validation, 1 for testing | CT | DL, CNN | DSC | Average dice coefficient of 0.89 |
| Qiu et al. [42] | 48 subjects | mandible | 52 scans for training, 8 for validation, 49 for testing | CT | DL, Single-planar CNN | DSC, 3D surface error | Average dice score of0.9328(± 0.0144), 95HD (mm) of 1.4333(± 0.5564) |
| Qiu, Guo, et al. [43] | 48 subjects | mandible | 90 scans for training, 2 for validation, 17 for testing | CT | DL Recurrent Convolutional Neural Networks for Mandible Segmentation (RCNNSeg) | DSC, ASD, 95HD | Average DSC of 97.48%, ASD of 0.2170 mm, and 95HD of 2.6562 mm |
| Qiu, van der Wel, et al. [44] | 59 subjects | mandible | 38 subject cases for training, 1 for validation, 20 for testing | CBCT | DL, SASeg | DSC, ASD, 95HD | Average DSC of 95.35 (± 1.54)%, ASD of0.9908 (± 0.4128) mm, and 95HD of 2.5723 (± 4.1192) mm |
| Qiu, van der Wel, et al. [8] | 59 subjects | mandible | 38 scans for training, 1 for validation, 20 for testing | CBCT | DL, 3D CNN and recurrent SegUnet | DSC, ASD, 95HD | Average DSC of 95.31 (± 1.11)%, ASD of 1.2827 (± 0.2780) mm, and 95HD of 3.1258 (± 3.2311) mm |
| Usman et al. [45] | NR | mandibular canal | 400 scans for training, 500 for testing | CBCT | DL, Attention-Based CNN model with Multi-Scale input Residual UNet (MSiR-UNet) | Dice ratio, mean IoU, | A dice score of 0.751, mean IoU of 0.795 |
| Verhelst et al. [46] | NR | mandible | 160 scans for training, 30 for testing | CBCT | DL, 2-stageed 3D U-Net | DSC, IoU, HD, | IoU of 94.6 ± 1.17%, DSC of 0.9722 ± 0.0062, HD (mm) of 4.1583 ± 2.8549, |
| Vinayahalingam et al. [47] | 81 subjects | mandibular condyles and glenoid f | 130 scans for training, 24 for validation, 8 for testing | CBCT | DL, 3D U-Net | Dice ratio, IoU, |
The dice ratio was 0.976 (0.01) and IoU was 0.954 (0.02) for mandibular condyle segmentation. The dice ratio was 0.966 (0.03) and IoU was 0.936 (0.04) for Glenoid fossa segmentation. |
| Wang et al. [7] | 30 subjects, 19 females, mean age of 14.2 ± 3.4 years | mandible, maxilla, teeth | 21 scans for training, 7 for testing | CBCT | DL, mixed-scale dense (MS-D) CNN | DSC, surface deviation | Dice similarity coefficient: 0.934 ± 0.019, jaw; 0.945 ± 0.021, teeth |
| Wang et al. [48] | 15 subjects, 9 females, mean age of 26 ± 10 years | mandible, maxilla | 30 scans for training, 15 scans for validation | CBCT | ML, patch-based sparse representation, and convex optimization | DSC | Dice ratio of 0.92 ± 0.02 for mandible segmentation and 0.87 ± 0.02 for maxilla segmentation. |
| Wang et al. [49] | 30 subjects, 18 females, mean age of 24 ± 10 years | mandible, maxilla | 30 scans for training, 30 CBCT scans, and 60 spiral multislice CT (MSCT) scans for validation | CBCT | ML, prior-guided sequential random forests | DSC | Dice ratio of 0.94 ± 0.02 for mandible segmentation and 0.91 ± 0.03 for maxilla segmentation |
DL, Deep learning, ML, Machine learning, CNN, convolutional neural network; DSC, Dice similarity Coefficient; HD, Hausdorff distance; 95HD, 95% Hausdorff distance; ASD, average surface distance; RVD, Relative Volume Difference; ASSD, Average Symmetric Surface Distance; JSC, Jaccard similarity coefficient, IoU, intersection over union; RMS, root mean square; MCD, mean curve distance; RVD, relative volume difference