Table 3.

Classification components in CADe schemes for detection of polyps in CT colonography

Study	Database	Classifier/Met hod	Performance
Gokturk et al. [121]	CTC data (2.5–3.0 mm collimation) of 48 patients in either supine or prone, containing 40 polyps (2–15 mm)	SVM with high-dimensional histograms used as shape signature	Sensitivity of 100% (95%) with a specificity of 0.69 (0.74) [14.3 FPs/patient (12.0 FPs/patient)]
Näppi et al. [122].	CTC data (5 mm collimation) of 40 patients in both supine and prone, including 12 polyps in 11 patients	LDA or QDA with 54 volumetric features (9 statistics of 6 features)	By-patient (by-polyp) sensitivity of 100% (95%) with 2.4 FPs/patient
Acar et al. [123]	CTC data (2.5–3.0 mm collimation) of 48 patients in either supine or prone, containing 40 polyps (2–15 mm)	QDA with edge-displacement fields	Sensitivity of 100% (95%) with a specificity of 0.47 (0.56)
Jerebko et al. [30]	CTC data (5 mm collimation) of 40 patients in both supine and prone, including 29 polyps (3–25 mm) in 20 patients	A multilayer perceptron with 4 features selected from 17 features	Sensitivity of 90% with a specificity of 95% with 32 FPs/patient
Jerebko et al. [124]	CTC data (5 mm collimation) of 40 patients in both supine and prone, including 21 polyps (5–25 mm)	A committee of multilayer perceptrons with 12 features	Sensitivity of 82.9% with a specificity of 95.3% with 5.4 FPs/patient
Jerebko et al. [125]	CTC data (5 mm collimation) of 40 patients in both supine and prone, including 21 polyps (5–25 mm)	A committee of SVMs with 9 selected features	Sensitivity of 86.7% for larger polyps (≥10mm) and 75% for other polyps with 3 FPs/patient in a independent test
Wang et al. [126]	CTC data (5 mm collimation) of 153 patients in both supine and prone, including 61 polyps (4–30 mm) in 45 patients	LDA with internal features of polyps	Sensitivity of 100% and 100% for larger polyps (≥10mm) and other polyps with 4 and 6.9 FPs/patient, respectively
Suzuki et al. [127]	CTC data (1.25–5 mm collimation) of 73 patients in both supine and prone, including 28 polyps (5–25 mm) in 15 patients	Bayesian ANN and a single 3D MTANN with voxel values in a 7×7×7 subvolume as input	By-polyp (by-patient) sensitivity of 96.4% (100%) with 2.1 FPs/patient in an LOO test of the classification part
Suzuki et al. [20]	CTC data (1.25–5 mm collimation) of 73 patients in both supine and prone, including 28 polyps (5–25 mm) in 15 patients	Bayesian ANN and a mixture of expert 3D MTANNs with voxel values in a 7×7×7 subvolume as input	By-polyp (by-patient) sensitivity of 96.4% (100%) with 1.1 FPs/patient in an LOO test of the classification part
Li et al. [128]	CTC data of 44 patients containing 45 polyps (6–9 mm)	SVM classifier with wavelet-based features	Sensitivity of 71% with 5.4 FPs/patient in a 4-fold cross-validation test of the classification part
Wang et al. [129]	CTC data (1.25–2.5 mm collimation) of 791 patients in both supine and prone, including 123 polyps (6–9 mm) and 25 polyps (≥10mm)	SVM with nonlinear dimensionality reduction (i.e., diffusion map and locally linear embedding)	Sensitivity of 83% for polyps (6–9 mm) with 9 FPs/patient
Yao at al. [130]	CTC data (1.25–2.5 mm collimation) of 792 patients in both supine and prone, including 226 polyps (> 6 mm)	SVM classifier with features from a topographic height map	Sensitivity of 93% and 76% for larger polyps (≥10 mm) and other polyps with 1.2 and 3.1 FPs/patient, respectively, in a 10-fold cross-validation test of the classification part
Suzuki et al. [66]	CTC data (1.25–5 mm collimation) of 24 patients in both supine and prone, including 23 polyps (6–25 mm) and a mass (35 mm), that had been “missed” by radiologists in a multicenter clinical trial [132]	Bayesian ANN and a mixture of expert 3D MTANNs with voxel values in a 7×7×7 subvolume as input	By-polyp (by-patient) sensitivity of 96.4% (100%) with 1.1 FPs/patient in an LOO test of the classification part
Zhou et al. [133]	CTC data (1.25–5.0 mm collimation) of 325 patients in supine and/or prone, including 347 polyps and masses (5–60 mm)	SVM classifier with projection features	By-polyp sensitivity of 93.1% and 80.6% for larger polyps (≥10mm) and other polyps with 1.9 and 5.2 FPs/patient, respectively
Wang et al. [134]	CTC data (1.25–2.5 mm collimation) of 66 patients in supine and/or prone, including 96 polyps	Multiple-kernel learning with statistical curvature and 18 geometric features	Sensitivity of 83% with 5 FPs/patient in an LOO test of the classification part