Fig 1.
An example of a low resolution, annotated echocardiographic image.
Fig 2.
An example of the cone segmentation task executed in a renal liver ultrasound.
a, the original image features all the technical elements of the ultrasound scan. b, the predicted mask from the cone segmentation superimposed on the ultrasound scan, c, the mask smoothed and applied over the image. d, finally a dilation step is applied to improve the cone segmentation.
Fig 3.
U-Net architecture implemented.
Table 1.
Architecture summary of U-Net implemented, parameters for each block and the total of parameters.
Fig 4.
An example of a mask from the right ventricle (top-left in pink labeled VD) surrounding the right atrium (bottom-left in violet labeled AD).
Fig 5.
Mask VD after the correction.
Fig 6.
The final result improved for visualization purposes.
Fig 7.
Some instances from the custom dataset with their respective ground truth masks.
Fig 8.
Left (GT): Mask generated by the clinician’s labeling. Center (PR): Mask predicted by the model. Right (CR): Predicted mask after heuristic correction.
Fig 9.
Visible differences between the PR mask and the CR mask.
Table 2.
Comparison of Dice coefficients between U-Net vs ground truth (GT) and after correction vs GT for each chamber mask.
Fig 10.
Loss history for the training (red) and validation (blue) phases.
Fig 11.
Dice Coefficient (dice_coeff), Intersection over Union (IoU) and mean pixel accuracy achieved in the validation phase.
Fig 12.
Two examples from the first case where the segmentations were very close to the ground truth.
It can be proved the good quality of the images for this case (especially on the contrast between the cavities chambers and the rest of the heart structure).
Fig 13.
There are larger left ventricles (b) and variable shapes for the rest of the cavities and less defined borders (a) for this two examples. The model achieved good segmentations for both.
Fig 14.
In this third case, the quality drop is notorious for this two examples: (a) and (b). This is reflected in how segmentation quality is lower compared to the two previous cases but the model does not lose shape sense and keeps them regular given the cavity.
Fig 15.
Two examples of the fourth case, where annotations are incorrect and additional areas have been marked.