A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets
By R. Castillo, E. Castillo, Rudy Guerra, V. Johnson, T. McPhail, A. Garg, T. Guerrero. Physics in Medicine and Biology, 2009
633 citations recorded by the source corpus.
Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/9970c00d8dfc4e336a36f534671ea7a458e7d8b8
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In Biomedical Engineering
- Nikos Paragios
- Aristeidis Sotiras
- M. Brady
- Pingkun Yan
- Mark Jenkinson
- Christos Davatzikos
- Richard S. Zemel
- Uwe Kruger
In Computer Vision and Pattern Recognition
- Nikos Paragios
- Luc Van Gool
- Jitendra Malik
- Dacheng Tao
- Ming-Hsuan Yang
- Trevor Darrell
- Yi Yang
- Andrew Zisserman
Citations in the corpus
- Randomized Optimum Models for Structured Prediction (2012, methodology): “Lung CT Registration Task: We obtained volumetric CT scans of pairs of lungs at different phases of the respiratory process, from 10 subjects (Castillo et al., 2009).”
- Deformable Medical Image Registration: A Survey (2013, methodology): “One may cite the POPI model [452] containing 40 landmarks in every frame of a 4 D lung CT acquisition, or the 4 D CT dataset made available by Castillo et al. [453] with landmarks in the maximum inhale and exhale phase.”
- Deep learning in medical image registration: a survey (2019, methodology): “ng. Therefore, we summarized the registration datasets for brain, registration accuracies for lung. 4.1 Lung DIRLAB is one of the most cited public datasets for 4D-CT chest image registration studies [12]. DIRLAB provides 300 manually selected landmark pairs for end-exhalation and end-inhalation pha”
- MIND: Modality independent neighbourhood descriptor for multi-modal deformable registration (2012, methodology): “We performed deformable registration on ten CT scan pairs between inhale and exhale phase of the breathing cycle, provided by the DIR-Lab at the University of Texas (Castillo et al., 2009).”
- Evaluation of Registration Methods on Thoracic CT: The EMPIRE10 Challenge (2011, neutral): “Analysis of point correspondence is a commonly used way to evaluate registration algorithms [13], [29]–[34].”
- Deep learning in medical image registration: a review (2019, methodology): “08 mm TRE on DIRLAB [12], which was much worse than 1.”
- MRF-Based Deformable Registration and Ventilation Estimation of Lung CT (2013, methodology): “We performed deformable registration on ten cases of the DIR-Lab 4DCT dataset acquired at inhale and exhale phase [42].”