# Comparison of Manual versus Semi-Automatic Segmentations of the Stenotic Carotid Artery Bifurcation
## 4 Discussion
### Effect of Differences of Segmentation  
The corroboration leads to some key conclusions. There were several habitual differences between the medical and non-medical examiners in segmentation processing and the choice of setting options. Some cornerstones, listed in the methods section, were established by the study working group to reduce the differences. Our aim was to reduce the differences attributed to the different experiences in segmentation and to measure the difference in lumen geometry between the two segmentation methods in the third (separated) group. The results show that over- and under-segmentation can be significantly reduced after the definition of cornerstones, but the differences caused by severe calcification can be reduced only with corroboration during the segmentation. The quantitative analysis revealed that the ADDV average and the standard deviation of the studied patient population were lower than what we measured in the first individual segmentation group and considerably larger than in the second and third groups. This result can indicate the usefulness of a collaboration between the medical and non-medical examiner, during the geometry preparation for a computational study. Manual thresholding might bias the risk of over/under-segmentation but elaborating on some cornerstones can decrease the associated errors. A larger risk of over/under-segmentation can occur locally if a high amount of calcification is present, for two reasons. Calcification blooming can easily distort the image locally, segmentation can be labor-intensive and mostly subjective, and manual oversight might cause/result differences at these locations. Another source of bias can originate from the shadowing effect of complex-shaped calcification. Sometimes the contrast medium may not be present at some almost enclosed lumen pockets. The external carotid artery exclusion could be a source of potential bias if several observers perform the lumen segmentation instead of consensus reading. Advanced semi-automatic imaging analysis protocols are on the horizon of daily clinical practice. However, the main advantage of semi-manual segmentation is the automatic centerline setting and the automatic modification of the centerline after manual correction, which can spare time for the examiner, but the external carotid artery exclusion plane is also automatically modified by the software. The manual segmentation allows the external carotid artery exclusion perpendicularly to the axis of the artery, and the centerline and minimum lumen diameter settings can be defined more precisely if the standardized window setting is applied. Complex stenotic lesions with severe calcification are challenging even for experienced investigators. A previous study that compared automatic versus manual segmentation in stenotic carotid artery lesions, showed some bias between the the observers andthe automated method. The differences between the automated and the manual method and also between the observers were significant for calcium volume [31]. The previously published articles differ in the HU threshold setting of calcifications (over 96--over 250 HU) for carotid artery plaque analysis studies based on CT angiography images [32; 33; 34; 35]. These notable discrepancies in the literature show that a wide and dynamic window setting must be used to precisely evaluate the lumen segmentation at the calcified part of the target vessel. Different plaque component analysis studies use different CT angiography settings and require different image quality, so calcification definitions based on only HU units can cause severe bias among studies. Furthermore, circular calcification can cause an artificial loss of automatically detectable contrast agent in pre-occlusive severe stenotic lumens. Due to frequent deletions of the residual lumen in calcified plaques, high-grade stenosis might be overestimated as occlusions, according to a previous study about automatic segmentation [19].