Fig. 1

Automatic pixel-wise LUS image classification based on semantic segmentation. LUS patterns are described following current guidelines [1]: A: normal; B: lung edema; C: alveolar consolidation; pleura: pleural line detection (please see Additional file 1 for additional information). a–c Four-panel views to allow the estimation of the perceptual quality of the fully automated classifier for each LUS pattern. Each 4-panel represents both the neural network input (right upper panel: raw LUS image; left upper panel: annotated LUS images) and output (right and left lower panels: heatmaps highlighting the area of the image that were most contributory to the model’s classification decision). e–g Confusion matrix computed for each LUS pattern (pleural line, 1-d; pattern A, 1-e; pattern B, 1-f; pattern C, 1-g) on a voxel-wise level. h The LUS final segmentation was very accurate (F1 = 0.966, Recall = 0.966; Precision = 0.966). Aires under the curve (AUC) values are depicted for of each LUS patterns detection