Figure 2 | Scientific Reports

Figure 2

From: A fast and fully-automated deep-learning approach for accurate hemorrhage segmentation and volume quantification in non-contrast whole-head CT

Figure 2

Examples showing the segmentation outcomes using the CNN-DS method. In each panel, the left, middle, and right images are the original CT slice, the ‘ground truth’ labels, and the CNN-DS predicted segmentation, respectively. The pointing arrows indicate the error. (A) Represents a case where the CNN-DS method demonstrates an expert-level performance. (B) Shows a false positive instance where a calcified structure is labelled as a hemorrhagic area due to its Hounsfield Unit values being higher than those of its surrounding tissues. (C) Shows a false negative example in which the CNN-DS method identified part of the hemorrhage but missed some blood close to the bone. (D) Illustrates a more complicated case of complex hemorrhage where the discrepancies between the ‘ground truth’ and the predicted segmentation cannot necessarily be attributed to erroneous prediction.

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