Table 2.
(a) Validation metrics of segmenting dense LV images with modified resnet-50-m backbone in DCNN
| Segment | Accuracya (%) | Good Contourb (%) | Dice Score | APD (mm) | Hausdorff Dist. (mm) | Sensitivity (%) | Specificity (%) |
|---|---|---|---|---|---|---|---|
| (a) | |||||||
| Validation Set | |||||||
| Chest cavity | 94 | 100 | 0.96 (0.02) | 1.6 (0.6) | 15.3 (3.8) | 94 (3) | 96 (4) |
| Surrounding frame | 95 | 99 | 0.93 (0.04) | 2.7 (1.1) | 15.7 (3.4) | 95 (4) | 95 (3) |
| LV myocardium | 98 | 100 | 0.93 (0.02) | 1.2 (0.4) | 5.5 (1.9) | 98 (2) | 100 (<1) |
| LV cavity | 93 | 100 | 0.93 (0.06) | 1.0 (0.4) | 4.4 (1.2) | 92 (5) | 100 (<1) |
| All | 95 | 100 | 0.93 (0.04) | 1.6 (0.8) | 10.2 (7.0) | 95 (4) | 98 (3) |
| Test Set | |||||||
| Chest cavity | 95 | 100 | 0.96 (0.02) | 1.7 (0.7) | 15.2 (3.9) | 94 (3) | 96 (4) |
| Surrounding frame | 96 | 98 | 0.93 (0.04) | 2.8 (1.2) | 15.8 (3.3) | 95 (4) | 96 (3) |
| LV myocardium | 97 | 100 | 0.92 (0.03) | 1.2 (0.4) | 5.5 (2.0) | 96 (4) | 100 (<1) |
| LV cavity | 93 | 100 | 0.91 (0.09) | 1.1 (0.6) | 4.7 (1.9) | 92 (6) | 100 (<1) |
| All | 95 | 100 | 0.93 (0.06) | 1.7 (0.9) | 10.3 (6.9) | 94 (5) | 98 (3) |
| (b) | |||||||
| Validation Set | |||||||
| Chest cavity | 95 | 99 | 0.96 (0.01) | 2.2 (0.9) | 14.7 (3.9) | 95 (3) | 95 (4) |
| Surrounding frame | 95 | 99 | 0.93 (0.04) | 2.3 (0.8) | 15.8 (3.2) | 95 (4) | 95 (3) |
| LV myocardium | 96 | 100 | 0.91 (0.02) | 1.7 (0.6) | 6.0 (1.7) | 96 (3) | 99 (<1) |
| LV cavity | 93 | 100 | 0.91 (0.07) | 1.3 (0.6) | 5.5 (1.7) | 91 (6) | 100 (<1) |
| All | 95 | 100 | 0.93 (0.05) | 1.9 (0.8) | 10.5 (6.5) | 94 (5) | 97 (3) |
| Test Set | |||||||
| Chest cavity | 95 | 99 | 0.96 (0.02) | 2.3 (1.0) | 14.7 (3.9) | 95 (3) | 95 (4) |
| Surrounding frame | 94 | 98 | 0.93 (0.04) | 2.4 (1.0) | 15.9 (3.4) | 93 (5) | 95 (3) |
| LV myocardium | 95 | 100 | 0.90 (0.03) | 1.7 (0.6) | 5.8 (2.2) | 94 (6) | 99 (<1) |
| LV cavity | 93 | 100 | 0.90 (0.10) | 1.3 (0.7) | 5.8 (1.8) | 91 (6) | 100 (<1) |
| All | 94 | 99 | 0.92 (0.06) | 1.9 (0.9) | 10.6 (6.5) | 93 (6) | 97 (4) |
| (c) | |||||||
| Validation Set | |||||||
| Chest cavity | 91 | 95 | 0.93 (0.06) | 2.5 (1.5) | 15.7 (3.2) | 90 (7) | 94 (4) |
| Surrounding frame | 93 | 84 | 0.90 (0.06) | 3.9 (2.2) | 15.9 (3.4) | 94 (6) | 91 (5) |
| LV myocardium | 96 | 100 | 0.87 (0.05) | 1.6 (0.7) | 6.7 (2.3) | 96 (6) | 100 (<1) |
| LV cavity | 87 | 100 | 0.87 (0.14) | 1.6 (0.9) | 5.8 (1.8) | 85 (11) | 100 (<1) |
| All | 92 | 95 | 0.89 (0.09) | 2.4 (1.7) | 11.0 (6.5) | 91 (9) | 96 (4) |
| Test Set | |||||||
| Chest cavity | 91 | 90 | 0.92 (0.07) | 2.8 (2.0) | 15.9 (3.2) | 90 (8) | 93 (5) |
| Surrounding frame | 92 | 82 | 0.89 (0.08) | 4.1 (2.5) | 15.9 (3.3) | 92 (8) | 91 (6) |
| LV myocardium | 94 | 99 | 0.86 (0.08) | 1.7 (1.0) | 6.9 (2.7) | 94 (6) | 100 (<1) |
| LV cavity | 85 | 97 | 0.85 (0.15) | 1.7 (1.2) | 5.7 (1.9) | 83 (13) | 100 (<1) |
| All | 90 | 92 | 0.88 (0.11) | 2.6 (2.0) | 11.1 (6.6) | 90 (11) | 95 (5) |
APD, Average perpendicular distance; DCNN, Deep convolutional neural network.
(b) Validation metrics of segmenting dense LV images with atrous resnet-50-a backbone in DCNN. (c) Validation metrics of segmenting dense LV images with non-atrous resnet-50-n backbone In DCNN.
a
Calculated as the % of correctly identified pixels for a class.
b
Calculated as % of contours with APD < 5mm. (): Standard deviations.