Fig. 4 | Scientific Reports

Fig. 4

From: Deep learning-assisted CBCT segmentation provides reliable volumetric assessment of mandibular defects compared with micro-CT for 3D printing and surgical planning

Fig. 4

Semi-automatic segmentation steps for volume calculation in ImageJ. (a) Binary mask of the mandible generated by Otsu thresholding. (b) Manual drawing of a straight line to define the outer contour of the defect. (c) An inverted reslice stack of the defect. (d) A cropped 3D model visualizing the anterior defect. (e) A reoriented 3D view of the segmented defect. (f) An overlay of the final segmented defect (green area) on the original binary mask.

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