TABLE 4:
Quantitative comparison of state-of-the-art segmentation methods. LV (Left Ventricle), A2C-A4C (apical 2–4 view), PLAX (parasternal long axis view), PSAX (parasternal short axis view), CW (continuous wave), PW (pulse wave), MV (mitral valve), AV (aortic valve), ED (end-diastole), ES (end-systole), RMSE (root mean square error), SRAD (speckle reduction anisotropic diffusion), PPG (peak pressure gradient), PHT (pressure half time), EPV (E-Wave Peak Velocity), EDT (E-Wave deceleration time), APV (A-Wave Peak Velocity), and ADU (A-Wave duration).
| Work | ROI Method | Mode & View | Method | System & Data | Ground Truth | Performance |
|---|---|---|---|---|---|---|
| [52] | Semi-automated | B-mode: A4C, LV | Low Level Image Processing Pre-processing module (Filtering & morphological operations) Segmentation module (Watershed and contour correction) | ATL HDI 3000; 12 volunteers, 12 vid. 44fps, 900 frames | Manual contours by a specialist | CC (Section 3.2): 0.87 High (0.99±0.01)Average (0.90±0.024) Low (0.73±0.101) |
| [54] | Automated (k-means) | B-mode: A4C, all chambers | Low Level Image Processing SRAD filtering, thresholding, edge detection | System: NA; 20 volunteers, 25 videos | Manual contours by a specialist | ** |
| [60] | NA | B-mode: A2C, LV | Deformable Model: Active contour, coupled optimization function | System: NA; 61 volunteers, 85 ED images | Manual contours for 85 images by expert echo | ** |
| [61] | NA | B-mode: PLAX, PSAX LV | Deformable model: Smoothing, Hough transform, active contour | System: NA; 11 volunteers, 15 ED images | Manual contours for 85 images by echo expert | ** |
| [62] | Manual | B-mode: A2C, A4C LV | Deformable Model: Control points located manually (initial contour) B-spline snake (final contour) | System: GE; Vivid 3; 50 ES and ED images | Manual contours and cardiac indices by echo expert | RMSE between auto and manual: LV area: 1.5, LV volume: 6.8, Ejection fraction: 4.6 |
| [72] | Manual | B-mode: A4C, LV LV | Statistical Model: Global despeckling, active appearance model training, | System: NA; synthetic and clinical echo images, 56 normal fetuses | Manual contours by cardiologist | Pixel accuracy (Section 3.1): Synthetic (84.12%), Clinical (84.39%) |
| [76] | NA | B-mode: A4C, all chambers | Conventional Machine Learning: Adaptive Group Dictionary Learning, Dictionary initialization, sparse group representation, pixel classification | System: NA; 40 clinical images of 50 normal fetuses | Manual contours by cardiologist | ** |
| [46] | NA | B-mode: A2C, A4C PLAX, PSAX; all chambers | Deep Learning (Pixel Segmentation): 4 U-net CNN models trained using images and masks (A2C = 198, A4C = 168, PSAX = 72, PLAX = 128); Augmentation (cropping & blackout); Training, 2 hours on Nvidia GTX 1080; Runtime: 110ms per image on average | System: NA; Train: 566 images and masks; Test: 557 images | Manual segmentation of all chambers | ** IOU value (Section 3.2): 55% to 92% for all views and chambers |
| [82] | Manual | Spectral Doppler; long strips | Low Level Image Processing: Objective thresholding method, morphological operations, biggest-gap algorithm for peak detection | GE Vivid 5; 25 CW & PW normal images | Manual velocity time integral & peak-velocity by cardiologist | CC (Section 3.2): velocity-integral (0.94) peak-velocity (0.98) |
| [83] | Detection based on axes fixed locations | Spectral CW Doppler; | Low Level Image Processing: Noise filtering & contrast adjustment, Canny edge detector, envelope smoothing, peak detector smoothing, peak detector | System: NA; 22 images; 11 normal subjects; 3 age groups | Manual peak velocity, PPG, and PHF by a cardiologist | CC (Section 3.2): Age G1 (20–35): 0.985, Age G2 (36–50): 0.922, Age G3 (51–65): 0.833 |
| [84] | Manual | Spectral CW Doppler | Low Level Image Processing: Texture filters (entropy, range, and standard deviation), thresholding, morphological operations | System: NA; 20 CW images; 25 patients with AR | Manual envelope contours by a cardiologist | ** |
| [63] | NA | Spectral Doppler, MV & AV | Deformable Model: Speckle resistant gradient vector flow, Generalized gradient vector flow field | Philips devices; 30 patients, 10 with atrial fibrillation, 20 normal | Manual velocity time integral, peak velocity, & border contours by 2 experts | CC and B&A (Section 3.2): see [63] for complete results |
| [93] | NA | Spectral CW Doppler | Model-based: Reference image calculated from all training images (model), mapping or registration from input to reference | GE Vivid 7; 59 CW images; 30 normal volunteers | Manual envelope delineation by echo expert | ** |
| [96] | Automated; 3 trained detectors | Spectral Doppler: MV | Conventional Machine Learning: E peak detector (left root), A velocity detector (right root), peak detector; training shape inference model (mapping from image to its shape) | System: NA; 255 training, 43 testing | Manual Doppler indices (EPV, EDT, (APV, ADU) by 2 sonographers | CC (Section 3.2): EPV (0.987), EDT (0.821), APV (0.986), EDU (0.481) |
**
indicates the performance is reported by superimposing the automated segmentation on raw images. We refer the reader to the actual papers for visualization of the results as including these images would require obtaining permission from the publisher.