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Fig. 1 | BMC Oral Health

Fig. 1

From: Automated cephalometric landmark detection with confidence regions using Bayesian convolutional neural networks

Fig. 1

Schematic of the overall detection framework. a Original lateral cephalogram (lat ceph) gets downsampled by a factor of 3. b From the downsampled lat ceph, image batches (\( I\left(\overrightarrow{p_l}\right) \)) are sampled with a stride of 3 mm along the width and the height direction from all over the lat ceph. c From the LRS calculation, CNN model provides a region of interest for the target landmark to be located in. d Every single pixel from the ROI is, again, sampled as an image batch (\( I\left(\overrightarrow{p_h}\right) \)) to be put into Bayesian CNN(B-CNN) model for iterative calculations. e HRS provides the final predicted target position for the target landmark

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