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. Author manuscript; available in PMC: 2018 Nov 1.
Published in final edited form as: IEEE Trans Med Imaging. 2017 Jun 28;36(11):2319–2330. doi: 10.1109/TMI.2017.2721362

Fig. 3.

Fig. 3

Predicted masks overlaid on the data for fetal brain MRI; the top images show the improvement of the predicted brain mask in different steps of the Auto-Net using 2.5D-CNN. The middle images show the improvement of the predicted brain mask in different steps of the Auto-Net using U-Net. The bottom left and right images show the predicted brain masks using BET and 3dSkullStrip, respectively. The right image shows the ground truth manual segmentation. Despite the challenges raised, our method (Auto-Net) performed very well and much better than the other methods in this application. The Dice coefficient, sensitivity, and specificity, calculated based on the ground truth for this case, are shown underneath each image in this figure.