In this way, the automatic initialization of segmentation mask is done in the consecutive image frames which will undergo the ACWE algorithm to produce segmented thyroids.
It is also a semi-automatic 2D segmentation algorithm just like the ACWE as the user needs to mark the regions as being thyroid and nonthyroid in the initialization phase.
The advantage of this algorithm over ACWE is that it is much faster than ACWE and the user can interact with the result of the segmentation (i.e., postsegmentation) and correct if any errors are present.
This step involves 3D reconstruction and volume computation of the segmented thyroid from ACWE and PBC using ImFusion  and GC using MeVisLab imaging tools.
The result of segmentation in four thyroid images using ACWE along with the user-initialized mask is shown in Figure 7, GC is shown in Figure 8, PBC is shown in Figure 9, RFC is shown in Figure 10, and CNN is shown in Figure 11.
In this section, the comparison of the accuracy of segmentation in the all the five algorithms (i.e., ACWE, GC, PBC, RFC, and CNN) in terms of DC is presented in Table 2.
The volume of the segmented thyroids from ACWE, GC, and PBC was computed after the 3D reconstruction using ImFusion and MeVisLab and is presented in Table 4.
All the algorithms performed differently on average where ACWE performed the best with an average DC of 0.800, PBC performed the worst with an average DC of 0.670, and GC performed relatively well with an average DC of 0.765.
ACWE produced undersegmented and oversegmented results in some cases as the contour evolution (set by the user) does not reach all the regions of thyroid (e.g., isthmus of thyroid) as well as due to the wrong initialization of the contour (this happens when the segmentation results from one image frame are used to segment the corresponding image frames).
This makes the algorithm less robust as compared to GC and ACWE. The computation time was around 10 seconds making it the fastest of all the algorithms.
These approaches had higher accuracies of segmentation as compared with ACWE, GC, and PBC.
The computation time of ACWE could be accelerated by reducing the image resolution and using different initialization shapes (e.g., ellipse as thyroid is elliptical in shape).