The hybrid method provides a more accurate way for predicting LNM using PET and CT.
While a treatment planning system can solve the optimization problem with given weights, adjusting the weights for high plan quality is performed by human.
Dose calculation accuracy using sCT images has been improved over the original CBCT images, with the average Gamma Index passing rate increased from 95. 4% to 97. 4% for 1 mm/1% criteria.
The fusion algorithm takes full advantage of the handcrafted features and the highest level CNN features learned at the output layer.
We set up a parameter tuning policy network (PTPN), which maps an CT image patch to an output that specifies the direction and amplitude by which the parameter at the patch center is adjusted.