Fetching the paper…

Reading the bibliography…

2020

Deform-GAN:An Unsupervised Learning Model for Deformable Registration

Zhang, Xiaoyue, Jian, Weijian, Chen, Yu et al.

Original
PDF
Post on X

Understand

Deformable registration is one of the most challenging task in the field of medical image analysis, especially for the alignment between different sequences and modalities.

In this paper, a non-rigid registration method is proposed for 3D medical images leveraging unsupervised learning.
To the best of our knowledge, this is the first attempt to introduce gradient loss into deep-learning-based registration.
The proposed gradient loss is robust across sequences and modals for large deformation.

Deform-GAN:An Unsupervised Learning Model for Deformable Registration · Around

Built on

“Mind: Modality independent neighbourhood descriptor for multi-modal deformable registration,”
Mattias P Heinrich, Mark Jenkinson, Manav Bhushan, Tahreema Matin, Fergus V Gleeson, Michael Brady, and Julia A Schnabel, · 2012
Earlier work this paper cites.
“Image-to-image translation with conditional adversarial networks,”
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros, · 2017
Earlier work this paper cites.
“An unsupervised learning model for deformable medical image registration,”
Guha Balakrishnan, Amy Zhao, Mert R Sabuncu, John Guttag, and Adrian V Dalca, · 2018
Earlier work this paper cites.

Similar

“Unsupervised learning for fast probabilistic diffeomorphic registration,”
Adrian V Dalca, Guha Balakrishnan, John Guttag, and Mert R Sabuncu, · 2018
Cited alongside, same era.
“Adversarial deformation regularization for training image registration neural networks,”
Yipeng Hu, Eli Gibson, Nooshin Ghavami, Ester Bonmati, Caroline M Moore, Mark Emberton, Tom Vercauteren, J Alison Noble, and Dean C Barratt, · 2018
Cited alongside, same era.
“Convolutional networks for biomedical image segmentation,”
O Ronneberger, P Fischer, and TU-net Brox,
Cited in the paper.

Then

“Adversarial similarity network for evaluating image alignment in deep learning based registration,”
Jingfan Fan, Xiaohuan Cao, Zhong Xue, Pew-Thian Yap, and Dinggang Shen, · 2018
Later among the works it cites.
“Unsupervised 3d end-to-end medical image registration with volume tweening network,”
Original
Tingfung Lau, Ji Luo, Shengyu Zhao, Eric I Chang, Yan Xu, et al., · 2019
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…