A deep learning approach for 2D ultrasound and 3D CT/MR image registration in liver tumor ablation

2021 ◽  
pp. 106117
Author(s):  
Wei Wei ◽  
Xu Haishan ◽  
Julian Alpers ◽  
Marko Rak ◽  
Christian Hansen
Keyword(s):  
Deep Learning ◽  
Image Registration ◽  
Liver Tumor ◽  
Tumor Ablation ◽  
Learning Approach ◽  
3D Ct ◽  
Mr Image ◽  
2D Ultrasound

An unsupervised deep learning approach for 4DCT lung deformable image registration

2020 ◽  
Author(s):  
Yabo Fu ◽  
Yang Lei ◽  
Tonghe Wang ◽  
Kristin Higgins ◽  
Jeffrey D. Bradley ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Registration ◽  
Deformable Image Registration ◽  
Learning Approach ◽  
Unsupervised Deep Learning

scholarly journals Quicksilver: Fast predictive image registration – A deep learning approach

NeuroImage ◽  
2017 ◽  
Vol 158 ◽  
pp. 378-396 ◽  
Author(s):  
Xiao Yang ◽  
Roland Kwitt ◽  
Martin Styner ◽  
Marc Niethammer
Keyword(s):  
Deep Learning ◽  
Image Registration ◽  
Learning Approach

scholarly journals Diffusion Tensor Driven Image Registration: A Deep Learning Approach

2020 ◽  
pp. 131-140
Author(s):  
Irina Grigorescu ◽  
Alena Uus ◽  
Daan Christiaens ◽  
Lucilio Cordero-Grande ◽  
Jana Hutter ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Registration ◽  
Diffusion Tensor ◽  
Learning Approach

scholarly journals 2D Ultrasound and 3D MR Image Registration of the Prostate for Brachytherapy Surgical Navigation

Medicine ◽  
2015 ◽  
Vol 94 (40) ◽  
pp. e1643 ◽  
Author(s):  
Shihui Zhang ◽  
Shan Jiang ◽  
Zhiyong Yang ◽  
Ranlu Liu
Keyword(s):  
Image Registration ◽  
Surgical Navigation ◽  
Mr Image ◽  
2D Ultrasound

scholarly journals A Semiautomated Deep Learning Approach for Pancreas Segmentation

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Meixiang Huang ◽  
Chongfei Huang ◽  
Jing Yuan ◽  
Dexing Kong
Keyword(s):  
Deep Learning ◽  
Geometric Modeling ◽  
Feature Representation ◽  
Learning Approach ◽  
3D Ct ◽  
The Poor ◽  
Key Innovation ◽  
Sampling Position ◽  
Pancreas Segmentation ◽  
Intensity Contrast

Accurate pancreas segmentation from 3D CT volumes is important for pancreas diseases therapy. It is challenging to accurately delineate the pancreas due to the poor intensity contrast and intrinsic large variations in volume, shape, and location. In this paper, we propose a semiautomated deformable U-Net, i.e., DUNet for the pancreas segmentation. The key innovation of our proposed method is a deformable convolution module, which adaptively adds learned offsets to each sampling position of 2D convolutional kernel to enhance feature representation. Combining deformable convolution module with U-Net enables our DUNet to flexibly capture pancreatic features and improve the geometric modeling capability of U-Net. Moreover, a nonlinear Dice-based loss function is designed to tackle the class-imbalanced problem in the pancreas segmentation. Experimental results show that our proposed method outperforms all comparison methods on the same NIH dataset.

Dynamic 2D Ultrasound and 3D CT Image Registration of the Beating Heart

2009 ◽  
Vol 28 (8) ◽  
pp. 1179-1189 ◽  
Author(s):  
Xishi Huang ◽  
J. Moore ◽  
G. Guiraudon ◽  
D.L. Jones ◽  
D. Bainbridge ◽  
...  
Keyword(s):  
Image Registration ◽  
Beating Heart ◽  
Ct Image ◽  
3D Ct ◽  
2D Ultrasound

State of the Art in Computer-Assisted Planning, Intervention, and Assessment of Liver-Tumor Ablation

2010 ◽  
Vol 38 (1) ◽  
pp. 31-52 ◽  
Author(s):  
Christian Schumann ◽  
Christian Rieder ◽  
Jennifer Bieberstein ◽  
Andreas Weihusen ◽  
Stephan Zidowitz ◽  
...  
Keyword(s):  
Liver Tumor ◽  
State Of The Art ◽  
Tumor Ablation ◽  
Computer Assisted ◽  
Planning Intervention
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