Mass Transportation for Deformable Image Registration with Application to Lung CT

2017 ◽  
pp. 66-74
Author(s):  
Bartłomiej W. Papież ◽  
Sir Michael Brady ◽  
Julia A. Schnabel
Keyword(s):  
Image Registration ◽  
Deformable Image Registration ◽  
Mass Transportation ◽  
Lung Ct

scholarly journals Biomechanical deformable image registration of longitudinal lung CT images using vessel information

2016 ◽  
Vol 61 (13) ◽  
pp. 4826-4839 ◽  
Author(s):  
Guillaume Cazoulat ◽  
Dawn Owen ◽  
Martha M Matuszak ◽  
James M Balter ◽  
Kristy K Brock
Keyword(s):  
Image Registration ◽  
Deformable Image Registration ◽  
Ct Images ◽  
Lung Ct

scholarly journals Inelastic Deformable Image Registration (i-DIR): Capturing Sliding Motion through Automatic Detection of Discontinuities

Mathematics ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 97
Author(s):  
Carlos I. Andrade ◽  
Daniel E. Hurtado
Keyword(s):  
Image Registration ◽  
Deformable Image Registration ◽  
Ct Images ◽  
Synthetic Image ◽  
Analysis Method ◽  
Biomedical Sciences ◽  
Sliding Motion ◽  
Lung Strain ◽  
Inelastic Shear ◽  
Lung Ct

Deformable image registration (DIR) is an image-analysis method with a broad range of applications in biomedical sciences. Current applications of DIR on computed-tomography (CT) images of the lung and other organs under deformation suffer from large errors and artifacts due to the inability of standard DIR methods to capture sliding between interfaces, as standard transformation models cannot adequately handle discontinuities. In this work, we aim at creating a novel inelastic deformable image registration (i-DIR) method that automatically detects sliding surfaces and that is capable of handling sliding discontinuous motion. Our method relies on the introduction of an inelastic regularization term in the DIR formulation, where sliding is characterized as an inelastic shear strain. We validate the i-DIR by studying synthetic image datasets with strong sliding motion, and compare its results against two other elastic DIR formulations using landmark analysis. Further, we demonstrate the applicability of the i-DIR method to medical CT images by registering lung CT images. Our results show that the i-DIR method delivers accurate estimates of a local lung strain that are similar to fields reported in the literature, and that do not exhibit spurious oscillatory patterns typically observed in elastic DIR methods. We conclude that the i-DIR method automatically locates regions of sliding that arise in the dorsal pleural cavity, delivering significantly smaller errors than traditional elastic DIR methods.

SU-E-J-91: Biomechanical Deformable Image Registration of Longitudinal Lung CT Images

2015 ◽  
Vol 42 (6Part8) ◽  
pp. 3285-3285
Author(s):  
G Cazoulat ◽  
D Owen ◽  
M Matuszak ◽  
J Balter ◽  
K Brock
Keyword(s):  
Image Registration ◽  
Deformable Image Registration ◽  
Ct Images ◽  
Lung Ct

A fast and scalable method for quality assurance of deformable image registration on lung CT scans using convolutional neural networks

2019 ◽  
Vol 47 (1) ◽  
pp. 99-109 ◽  
Author(s):  
Shaikat M. Galib ◽  
Hyoung K. Lee ◽  
Christopher L. Guy ◽  
Matthew J. Riblett ◽  
Geoffrey D. Hugo
Keyword(s):  
Neural Networks ◽  
Quality Assurance ◽  
Image Registration ◽  
Convolutional Neural Networks ◽  
Deformable Image Registration ◽  
Ct Scans ◽  
Lung Ct

DIRBoost–An algorithm for boosting deformable image registration: Application to lung CT intra-subject registration

2014 ◽  
Vol 18 (3) ◽  
pp. 449-459 ◽  
Author(s):  
Sascha E.A. Muenzing ◽  
Bram van Ginneken ◽  
Max A. Viergever ◽  
Josien P.W. Pluim
Keyword(s):  
Image Registration ◽  
Deformable Image Registration ◽  
Lung Ct

OC-0444: Impact of deformable image registration on inter-fractional variations in lung cancer proton therapy

2020 ◽  
Vol 152 ◽  
pp. S245
Author(s):  
L. Nenoff ◽  
C.O. Ribeiro ◽  
M. Matter ◽  
L. Hafner ◽  
A.C. Knopf ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Image Registration ◽  
Proton Therapy ◽  
Deformable Image Registration

scholarly journals Detection of vessel bifurcations in CT scans for automatic objective assessment of deformable image registration accuracy

2021 ◽  
Author(s):  
Guillaume Cazoulat ◽  
Brian M Anderson ◽  
Molly M McCulloch ◽  
Bastien Rigaud ◽  
Eugene J Koay ◽  
...  
Keyword(s):  
Image Registration ◽  
Objective Assessment ◽  
Deformable Image Registration ◽  
Ct Scans ◽  
Registration Accuracy ◽  
Vessel Bifurcations

PO-1508: Exploration of deformable image registration to augment training data for deep learning contouring

2020 ◽  
Vol 152 ◽  
pp. S814
Author(s):  
R. Baggs ◽  
P. Aljabar ◽  
M. Gooding ◽  
P. Poortmans ◽  
Y. Kirova
Keyword(s):  
Deep Learning ◽  
Image Registration ◽  
Deformable Image Registration ◽  
Training Data

scholarly journals Tissue-specific deformable image registration using a spatial-contextual filter

2021 ◽  
Vol 88 ◽  
pp. 101849
Author(s):  
Yongbin Zhang ◽  
Lifei Zhang ◽  
Laurence E. Court ◽  
Peter Balter ◽  
Lei Dong ◽  
...  
Keyword(s):  
Image Registration ◽  
Deformable Image Registration ◽  
Tissue Specific

scholarly journals Evolutionary Machine Learning for Multi-Objective Class Solutions in Medical Deformable Image Registration

Algorithms ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 99 ◽  
Author(s):  
Kleopatra Pirpinia ◽  
Peter A. N. Bosman ◽  
Jan-Jakob Sonke ◽  
Marcel van Herk ◽  
Tanja Alderliesten
Keyword(s):  
Machine Learning ◽  
Image Registration ◽  
State Of The Art ◽  
Deformable Image Registration ◽  
Optimization Approach ◽  
High Quality ◽  
Trade Off ◽  
Multi Objective ◽  
Current State ◽  
Image Artefacts

Current state-of-the-art medical deformable image registration (DIR) methods optimize a weighted sum of key objectives of interest. Having a pre-determined weight combination that leads to high-quality results for any instance of a specific DIR problem (i.e., a class solution) would facilitate clinical application of DIR. However, such a combination can vary widely for each instance and is currently often manually determined. A multi-objective optimization approach for DIR removes the need for manual tuning, providing a set of high-quality trade-off solutions. Here, we investigate machine learning for a multi-objective class solution, i.e., not a single weight combination, but a set thereof, that, when used on any instance of a specific DIR problem, approximates such a set of trade-off solutions. To this end, we employed a multi-objective evolutionary algorithm to learn sets of weight combinations for three breast DIR problems of increasing difficulty: 10 prone-prone cases, 4 prone-supine cases with limited deformations and 6 prone-supine cases with larger deformations and image artefacts. Clinically-acceptable results were obtained for the first two problems. Therefore, for DIR problems with limited deformations, a multi-objective class solution can be machine learned and used to compute straightforwardly multiple high-quality DIR outcomes, potentially leading to more efficient use of DIR in clinical practice.

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