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Lesions were semi-automatically segmented on each interval image series and supervised by a radiologist.

Each training utterance was automatically segmented to have about 30 s of speech.

In addition, NFL thickness was automatically segmented per A-scan on all volumes with another method based on adaptive thresholding [ 20– 20].

First, the fiducial markers were automatically segmented in the 3D preoperative volume based on knowledge about marker form, size, and attenuation.

These shifted maps are automatically segmented in three areas as well.

In addition, LVM was automatically segmented from the CCTA images.

> -wrap-foot> Alesionsons were automatically segmented by an operator (Y.T).

As a first step, the lungs were automatically segmented using methods based on region growing and morphological operations [ 12].

With this method, the circumferential distribution of the lipid plaques/FC was first identified by human analysts in all frames containing FC, then the FC boundaries in the radial dimensions were automatically segmented and volumetric quantification was performed on the segmented FC.

Each of the thirty-two spots in the data set was automatically segmented twice, once using the algorithm trained on pathologist A's manual segmentation masks and once using the algorithm trained on pathologist B's manual segmentation masks.

Because B-scans were not realigned (in depth or laterally) before analysis, misaligned B-scans did not match with the automatically segmented interfaces due to the restrictions placed on the interfaces' 3D shapes by the regularization procedure.