A Strictly Bounded Deep Network for Unpaired Cyclic Translation of
  Medical Images
Swati Rai Jignesh S. Bhatt Sarat Kumar Patra
Medical image translation is an ill-posed problem. Unlike existing paired
unbounded unidirectional translation networks, in this paper, we consider
unpaired medical images and provide a strictly bounded network that yields a
stable bidirectional translation. We propose a patch-level concatenated cyclic
conditional generative adversarial network (pCCGAN) embedded with adaptive
dictionary learning. It consists of two cyclically connected CGANs of 47 layers
each; where both generators (each of 32 layers) are conditioned with
concatenation of alternate unpaired patches from input and target modality
images (not ground truth) of the same organ. The key idea is to exploit
cross-neighborhood contextual feature information that bounds the translation
space and boosts generalization. The generators are further equipped with
adaptive dictionaries learned from the contextual patches to reduce possible
degradation. Discriminators are 15-layer deep networks that employ minimax
function to validate the translated imagery. A combined loss function is
formulated with adversarial, non-adversarial, forward-backward cyclic, and
identity losses that further minimize the variance of the proposed learning
machine. Qualitative, quantitative, and ablation analysis show superior results
on real CT and MRI.