Deep Neural Network Architecture
NCC
The DIRNet deep learning model proposed by de Vos et al. [The spatial transformer creates a displacement vector field (DVF) that allows the resampler to wrap a moving image around a fixed image. The wrapped image is compared to the fixed image through a normalized cross-correlation (NCC) as a similarity metric which was employed as the loss function. The DIRNet is trained through optimizing a backpropagating dissimilarity between pairs of moving and fixed images using the mini-batch stochastic gradient descent (Adam) algorithm [Training workflow of the DIRNet architecture. The DIRNet model takes a concatenated pair of fixed and deformed images as inputs. The convolutional regressor generates a grid of control points by analyzing the spatial correspondence between the input images. The spatial transformer generates a displacement vector field that enables the ReSampler to wrap the moving image to the fixed image. Solid lines indicate the baseline DIRNet and the blue dash-line indicates the proposed modification for the loss calculation in the backpropagating flow