@article{(Open Science Index):https://publications.waset.org/pdf/10011399,
	  title     = {NANCY: Combining Adversarial Networks with Cycle-Consistency for Robust Multi-Modal Image Registration},
	  author    = {Mirjana Ruppel and  Rajendra Persad and  Amit Bahl and  Sanja Dogramadzi and  Chris Melhuish and  Lyndon Smith},
	  country	= {},
	  institution	= {},
	  abstract     = {Multimodal image registration is a profoundly complex
task which is why deep learning has been used widely to address it in
recent years. However, two main challenges remain: Firstly, the lack
of ground truth data calls for an unsupervised learning approach,
which leads to the second challenge of defining a feasible loss
function that can compare two images of different modalities to judge
their level of alignment. To avoid this issue altogether we implement a
generative adversarial network consisting of two registration networks
GAB, GBA and two discrimination networks DA, DB connected by
spatial transformation layers. GAB learns to generate a deformation
field which registers an image of the modality B to an image of the
modality A. To do that, it uses the feedback of the discriminator DB
which is learning to judge the quality of alignment of the registered
image B. GBA and DA learn a mapping from modality A to modality
B. Additionally, a cycle-consistency loss is implemented. For this,
both registration networks are employed twice, therefore resulting in
images ˆA, ˆB which were registered to ˜B, ˜A which were registered
to the initial image pair A, B. Thus the resulting and initial images
of the same modality can be easily compared. A dataset of liver
CT and MRI was used to evaluate the quality of our approach and
to compare it against learning and non-learning based registration
algorithms. Our approach leads to dice scores of up to 0.80 ± 0.01
and is therefore comparable to and slightly more successful than
algorithms like SimpleElastix and VoxelMorph.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {14},
	  number    = {8},
	  year      = {2020},
	  pages     = {300 - 304},
	  ee        = {https://publications.waset.org/pdf/10011399},
	  url   	= {https://publications.waset.org/vol/164},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 164, 2020},