TABLE 3:
Quantitative comparison of automated methods for mode/view classification. A2C-A3C-A4C-A5C (apical 2–3-4–5 views), PLAX (parasternal long axis view), PSAX (parasternal short axis view), PSA (Parasternal Short Axis), PSAM-PASAP (PSA of Mitral and Papillary), IVC (inferior vena cava), SC2C-SC4C-SCLX (subcostal 2–4 chamber and long axis), GSAT (Gray- Level Symmetric Axis Transform), SVM (support vector machines), TPR (True Positive Rate).
| Work | ROI Method | Mode & View | Method | System & Data | Train & Test | Ground Truth | Performance |
|---|---|---|---|---|---|---|---|
| [32] | NA | B-mode: A2C, A4C, PALX, PSAX, SC2C, SC4C, SCLX, other | Conv. ML method GIST descriptor, probabilistic SVM | Philips CX50; 33Hz; 270 videos, 5–10 heartbeats | Train: 2700, Test: 2700 frames | Domain expert annotation for all views | TPR (Section 3.1): A2C-A4C (100), SAX (100), LAX (98), SC2 (96), SC4 (100), SCL (64), other (96) |
| [35] | LV Detectors (MLBoost) in all viwes | B-mode: A2C, A4C, PALX, PSAX | Conv. ML method Fusion of LV detectors; multi-class boosting | System: NA; 1303 videos, A2C (371), A4C (574), PSAX (203), PLAX (155) | Train: 1080; Test: A2C (61), A4C (96), PSAX (28), PLAX (38) | Manually localized LV regions | TPR (Section 3.1): A2C (93.5), A4C (97.9), PSAX (96.4), PLAX (97.4) |
| [36] | GSAT Detector | B-mode: A4C, PALX, PSAX | Conv. ML method Relational Structures, Markov Random Field, multi-class SVM | System: NA; 15 normal vid., 2657 i-frames; 6 abnormal, 552 i-frames; | Train: 2657, leave-one-out; Test: 552 | Domain expert annotation for all views | Average precision (Section 3.1): 88.35% |
| [37] | Manual | B-mode: A2C, A3C, A4C, A5C, SAB, SAP, PLA, PSAM | Conv. ML method Optical flow, edge-filtered map, SIFT features, SVM | System: NA 113 vid., 25 Hz 320×240 pix. 2470 frames | leave-one-out; | Manual labeling | TPR (Section 3.1): A2C (51), A3C (54), A4C (93), A5C (61), SAB (1.0), SAP (93), PLA (88), PSAM (71) |
| [46] | NA | B-mode: A2C, A3C, A4C, PLAX, PSAX, IVC, other | Deep learning: VGG-based CNN with 6 classes; ADAM, 64 batch 1 x 105 learning rate, 10–20 epochs; 2 hr training (GTX 1080), 600 ms runtime | System: NA > 4000 studies | Train: 40,000 images; Test: VC (159), A2C (555), A3C (174), A4C (756), PLAX (515), PSAX (458) | Manual labeling | TPR (Section 3.1): IVC (100), A2C (94) A3C (93), A4C (98), PLAX (99), PSAX (99.5) |
| [49] | NA | B-mode: A2C, A4C, PLAX, PSAX, ALAX, SC4C, SCVC, unknown | Deep learning: Inception-based CNN with 7 classes; Adam, 10−4 rate, 64 mini-batch, 100 epochs | GE Vivid E9, 4582 vid., 205 patients, avg. age: 64; GE Vivid E7, 2559 vid., 265 patients, avg. age: 49 | Train: 4582 vids., 256,649 frames; Test: 2559 vids., 229,951 frames | Manual labeling | Overall accuracy:Frame (98.3 ± 0.6) Video (98.9 ± 0.6); runtime (4.4 ± 0.3) ms (GPU) |
| [50] | NA | B-mode: 12 apical, parasternal, subcostal, suprasternal views | Deep learning: Lightweights VGG, DenseNet, and ResNet based models; ADAM, 1−4 rate, 300 batch | Philips, GE, and Siemens systems; 3,151 patients, 16,612 cines, 807908 frames | Patient level split: Train (60%), Valid (20%), Test (20%), | Manual labeling by senior cardiologist | Overall accuracy: 88.1%; fusion of 3 models |
| [51] | NA | B-mode: A2C, A3C, A4C, A5C, PLAX, PSAX, PSAM, PSAP | Deep learning: Spatial CNN, input: raw image; Temporal CNN, input: acceleration image | GE Vivid 7 or E9; 432 vid.; age: 7–85; 434×636, 26fps 341×415, 26fps | Train: 280, Test: 152; Re-sized: 227×227×26 frames | Clinicians in 2 hospitals labeled 8 views | TPR (Section 3.1): A2C:100, A3C:100, A4C:100, A5C:71.4, PLAX:96, PSAX:95, PSAM:88, PSAP:75 |