Table 1 Segmentation methods in chest X-ray. The datasets, methods, assessment measures, and segmentation results are provided in each column, respectively
From: Computer-aided detection in chest radiography based on artificial intelligence: a survey
Study | Datasets | Assessment measures | Results | |
|---|---|---|---|---|
Image progressing based methods | Cheng et al. [34] | Custom | Accuracy | |
Armato et al. [35] | Custom (600) | Subjectively assessed the accuracy and completeness of the contour | Up to 79.1% (score 4 or 5) and 8.1% inaccurate (score 1 or 2) | |
Li et al. [36] | Custom (40) | Accuracy, sensitivity, specificity | Left lung: 95.2% accuracy, 91% sensitivity, 96.5% specificity; right lung: 96% accuracy, 91.1% sensitivity, 97.2% specificity | |
Iakovidis et al. [37] | Custom (24) | Accuracy, sensitivity, and specificity | 95.3% sensitivity, 94.3% specificity | |
Wan et al. [38] | JSRT Custom (154) | Accuracy, overlap scores, precision, sensitivity, specificity, and F score | Accuracy, F value, accuracy, sensitivity, and specificity were higher than 90%; the JSRT dataset overlap score was 87%; the overlap rate of the custom datasets (standard machines) was 81% and (mobile machines) is 69% | |
Van Ginneken et al. [42] | Custom (230) | Overlap scores | Left lung: 0.887 ± 0.114; right lung: 0.929 ± 0.026 | |
Machine learning based methods | Mcnittgray et al. [45] | Custom (33) | Accuracy | NN: 76%; LDA: 70%; KNN: 70% |
Vittitoe et al. [46] | Custom (198) | Sensitivity, specificity, and accuracy | Sensitivity: 0.907 ± 0.044; specificity: 0.972 ± 0.022; accuracy: 0.948 ± 0.016 | |
Shi et al. [47] | JSRT (52) | Accuracy | 0.978 ± 0.0213 | |
Novikov et al. [51] | JSRT | Dice coefficient, jaccard coefficient | Lung: 97.4%, 95%; collarbone: 92.9%, 86.8%; heart: 93.7%, 88.2% | |
Dai et al. [52] | JSRT, MC | Intersection-over-union | Both lungs: 94:7% ± 0:4%, heart: 86:6% ± 1:2% |