Fig 1.
Fetal ultrasound images of normal (A) and cystic hygroma (B) scans.
Fig 2.
Identifying image annotations on a normal NT scan.
(A) Image annotations included calipers, text, icons, and profile traces, all of which were removed prior to model training. (B) 3D Scatter Plot of HSV image data. Each point represents one image pixel and its associated HSV values. The red region highlights the range of values which do not belong to the grayscale ultrasound image. The area encircled in green shows pixel values that belong to the grayscale ultrasound image. Grayscale images had H, S and V values ranging from 0–27, 0–150 and 0–255, respectively.
Fig 3.
Removal of image annotations on a scan with cystic hygroma diagnosis.
(A) Ultrasound image before annotations were removed. Yellow calipers (bottom middle) are visible, along with text annotations (top left). (B) The binary mask of the image which was generated to define the region of the image that need to be infilled (white pixels). (C) Result of the Navier-Stokes image infill method; all image annotations have been removed.
Fig 4.
Grad-CAM image of a cystic hygroma case.
The green gridlines indicate the size of the feature maps (8x8) used to generate the heat maps. The red highlights the region of the image that influenced the model’s prediction the most.
Table 1.
Partitioning of data across training and validation datasetsa.
Fig 5.
Receiver operating characteristic plot summarizing performance of all four cross validation folds.
Table 2.
4-fold cross validation resultsa.
Fig 6.
Grad-CAM heat maps for the full validation set of Fold 2.
Top 4 rows are normal NT cases and bottom 4 rows are cystic hygroma cases. Red colours highlight regions of high importance and blue colours highlight regions of low or no importance. Therefore, a good model would have Grad-CAM heatmaps that highlight the head and neck area for both normal and cystic hygroma images.
Fig 7.
(A) Normal NT case with good localization in which the model predicted the correct class with a high (1.00) output probability (true negative). (B) Cystic hygroma case with good localization in which the model predicted the correct class with a high (1.00) output probability (true positive). (C) Normal NT case showing poor localization in which the model predicted this class incorrectly with a 0.90 output probability (false positive). (D) Cystic hygroma case showing poor localization in which the model predicted the correct class, but with an output probability that suggests uncertainty (0.63) (true positive).