A fast and scalable method for quality assurance of deformable image registration on lung CT scans using convolutional neural networks-Reference-Cited by-同舟云学术

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

Published:2019-11-19 Issue:1 Volume:47 Page:99-109
ISSN:0094-2405
Container-title:Medical Physics
language:en
Short-container-title:Med. Phys.

Author:

Galib Shaikat M.¹,Lee Hyoung K.¹,Guy Christopher L.²,Riblett Matthew J.²,Hugo Geoffrey D.³

Affiliation:

1. Department of Nuclear Engineering Missouri University of Science and Technology Rolla MO 65409 USA

2. Department of Radiation Oncology Virginia Commonwealth University Richmond VA 23298 USA

3. Department of Radiation Oncology Washington University School of Medicine St. Louis 63110 MO USA

Publisher

Wiley

Subject

General Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/mp.13890

Reference39 articles.

1. PluimJP MuenzingSE EppenhofKA MurphyK.The truth is hard to make: Validation of medical image registration. In2016 23rd International Conference on Pattern Recognition (ICPR). IEEE;2016:2294–2300.

2. Image similarity and tissue overlaps as surrogates for image registration accuracy: widely used but unreliable;Rohlfing T.;IEEE Trans Med Imaging

3. Error estimation of deformable image registration of pulmonary CT scans using convolutional neural networks

4. A neural network approach for fast, automated quantification of DIR performance

5. The distribution of target registration error in rigid-body point-based registration

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