Table 3.
Deep learning-based lung nodule segmentation architectures and their key information.
| Study | Year | Architecture | Dataset | Approach | Performance |
|---|---|---|---|---|---|
| Pezzano et al. [65] | 2021 | CoLe-CNN | LIDC-IDRI | 2D Based U-Net Inception-v4 architecture Mean Square Error function |
F1 = 86.1 IoU = 76.6 |
| Dong et al. [67] | 2020 | MV-SIR | LIDC-IDRI | 2D/3D Residual block Secondary input Multi views Voxel heterogeneity (VH) Shape heterogeneity (SH) |
ASD = 7.2 ± 3.3 HSD = 129.3 ± 53.3 DSC = 92.6 ± 3.5 PPV = 93.6 ± 2.2 SEN = 98.1 ± 11.3 |
| Keetha et al. [54] | 2020 | U-DNet | LUNA16 | 2D Based U-Net Bi-FPN Efficient-Det Mish activity function |
DSC = 82.82 ± 11.71 SEN = 92.24 ± 14.14 PPV = 78.92 ± 17.52 |
| Cao et al. [72] | 2020 | DB-ResNet | LIDC-IDRI | 2D/3D ResNet CIP Multiview Multiscale Central Intensity-Pooling |
DSC = 82.74 ± 10.19 ASD = 19 ± 21 SEN = 89.35 ± 11.79 PPV = 79.64 ± 13.34 |
| Kumar el al. [55] | 2020 | V-Net | LUNA16 | 3D V-Net PReLU Only fully convolutional lays |
DSC = 96.15 |
| Usman et al. [56] | 2020 | Adaptive ROI with Multi-view Residual Learning | LIDC-IDRI | 2D/3D the Deep Residual U-Net Adaptive ROI Multiview |
SEN = 91.62 PPV = 88.24 DSC = 87.55 |
| Tang et al. [74] | 2019 | NoduleNet | LIDC-IDRI | 3D Multitask Residual-block Detection, FPR, segmentation Different loss function |
DSC = 83.10 CPM = 87.27 |
| Huang et al. [57] | 2019 | Faster R-CNN | LUNA16 | 2D Faster RCNN Merge overlap FP reduction Based FCN |
ACC = 91.4 DSC = 79.3 |
| Aresta et al. [58] | 2019 | iW-Net | LIDC-IDRI | 3D Based U-Net two points in the nodule boundary none heavy pre-processing steps augmentation |
IoU = 55 |
| Hesamian et al. [75] | 2019 | Atrous convolution | LIDC-IDRI | 2D Atrous convolution Residual Network Weight loss Normalize to 0, 255 |
DSC = 81.24 Precision = 79.75 |
| Liu et al. [76] | 2018 | Mask R-CNN | LIDC-IDRI | 2D Backbone: ResNet101, FPN transfer learning RPN FCN |
73.34 mAP 79.65 mAP |
| Khosravan et al. [77] | 2018 | Semi-supervised multitask learning | LUNA16 | 3D Data augmentation Semi-supervised FP reduction |
SEN = 98 DSC = 91 |
| Wu et al. [53] | 2018 | PN-SAMP | LIDC-IDRI | 3D 3D U-Net WW/WC Dice coefficient loss Segmentation, classification |
DSC = 73.98 |
| Tong et al. [59] | 2018 | Improved U-NET network | LUNA16 | 2D U-Net Modify residual block Obtain lung parenchyma |
DSC = 73.6 |
| Zhao et al. [60] | 2018 | 3D U-Net and Contextual Convolutional Neural Network | LIDC-IDRI | 3D 3D U-Net GAN Morphological methods Residual block Inception structure |
None |
| Wang et al. [66] | 2017 | MV-CNN | LIDC-IDRI | 2D/3D Mutilview A multiscale patch strategy |
SEN = 83.72 PPV = 77.59 DSC = 77.67 |
| Wang et al. [69] | 2017 | CF-CNN | LIDC-IDRI/GDGH | 2D/3D Central pooling 3D patch 2D views A sampling method Two datasets |
LIDC: DSC = 82.15 ± 10.76 SEN = 92.75 ± 12.83 PPV = 75.84 ± 13.14 GDGH: DSC = 80.02 ± 11.09 SEN = 83.19 ± 15.22 PPV = 79.30 ± 12.09 |