Automatic estimation of a resected liver region using a tumor domination ratio - Emerging Trends in Image Processing, Computer Vision, and Pattern Recognition - page 310

Table 5
Results of the Optimization Considering the Practical Conditions for Sample 1

Manual Approach by a Sur-
geon

Proposed (Radius: Proposed (Radius:

Sample 1

6 mm) 8 mm)

Resected volume (cc) 192 112 178

The number of cut-
points

1 2 1

Results Resected region Resected region Resected region

Table 6
Results of the Optimization Considering the Practical Conditions for Sample 2

Manual Approach by a Sur-
geon

Proposed (Radius Proposed (Radius

Sample 2

6 mm) 8 mm)

Resected volume (cc) 352 170 346

The number of cut-
points

2 3 2

Results Resected region Resected region Resected region

Table 7 shows the computation times for samples 1 and 2 and their details. Note that the op-
timization considering a vessel radius and a branch point is included in “computing resected
region.” Although this optimization requires additional computation time, the computation
time is still acceptable since it is much smaller than the time for planning by surgeons.

Table 7
Computation Time for Estimating the Resected Regions Considering a Vessel Radius and
Branch-Point

Processing Sample 1 Sample2

Computing perfused regions(s) 58 87

Computing resected region(s) 30 82

3D reconstruction(s) 18 16

Total(s) 106 185