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The use of dedicated iterative MAR over all four patients reduced the mean segmented artifact volume and segmentation time by 79.8% (4.2 ± 0.9 ml) and 40.1% (154 ± 38.6 s), respectively (Table 2).
The use of iterative MAR reduced segmentation time by 67.4% (from 310 to 101 s).
This makes the segmentation time very difficult to predict or to control.
The segmentation time is virtually independent from the density of the mesh.
This shows that the segmentation time and the number of iterations are nearly independent from the contents of the images.
Table 5 Average off-line training time and on-line segmentation time (in seconds) of SKI and Chen's method.
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This puts them in the most distant location possible from the objects of interest, which in turn results in longer segmentation times.
What is also very notable is that the WMD model delivered very similar segmentation times in all the scenarios, whereas the TAV model showed a much higher variety in this value.
With our previously established interactive method, the total segmentation times took 20 to 25 min per animal (manual delineation can take 40 to 45 min), whereas the proposed method only takes 2 min at most.
On the other hand, in the case of TAV, the distance that the external nodes need to travel during the segmentation task depends highly on the contents of the image, which results in a big variation of the segmentation times for different parts of the mesh.
Translation from the research setting to clinical practice of this technique is significantly hampered by practical issues; apart from the need to move the patient from the intensive care unit (ICU) to the CT facility, the necessary high ionizing radiation exposure, and long post-processing segmentation times are important hurdles.
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