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Table 1: Overview of the combined algorithm
Algorithm 1: Combined Algorithm
Require: X: CT volume, shape = D × H × W
Require: x_i=X(L_i), (i=1, .…, k): CT voxel patch
Require: y_i = M(x_i): y_i is the output of the last layer (softmax activation function) of the model M, y_i has 1 more dimension than x_i, and this dimension has 3 channels. Each channel refers to the probability of the corresponding voxel belonging to background or bone or nerve, respectively.
1) Initialize: Y ← 0
2) For x_i ∈ X,(i = 1, …, k) do
3) Y(L_i,:) + = y_i
4) End for
5) S ← arg max(Y, axis = −1) (find the channel with the largest value in the last dimension)
6) Return S (the automatic mask)
Note:—M indicates the model (network); L, location of the CT voxel patch x at the CT volumn X; Y, summed probability; max, maximum.