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01ca8ac Aug 17, 2018
Valery Vishnevskiy Update from gitlab
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We report the following accuracy of pTV on 4DCT and COPDgene datasets:

4DCT 1 4DCT 2 4DCT 3 4DCT 4 4DCT 5 4DCT 6 4DCT 7 4DCT 8 4DCT 9 4DCT 10 Mean TRE Mean Time (sec.)
0.77 0.75 0.93 1.26 1.07 0.83 0.80 1.01 0.91 0.84 0.92 130
Table: TRE is computed in the snap-to-voxel fashion. The code for this experiment is provided in examples_ptv/DIR_test_all.m
COPD 1 COPD 2 COPD 3 COPD 4 COPD 5 COPD 6 COPD 7 COPD 8 COPD 9 COPD 10 Mean TRE Mean Time (sec.)
0.71 1.91 0.77 0.67 0.71 0.66 0.75 0.78 0.64 0.85 0.8461 442
Table: TRE is computed in the snap-to-voxel fashion. The code for this experiment is provided in examples_ptv/COPD_finetune.m

For 4DCT dataset the method is configured as follows:

  • images are resampled to 1x1x1 mm^3 resolution
  • displacements control point grid spacing: 4x4x4 pixels
  • LCC kernel sigma is 2.1 mm
  • isotropic TV regularization weight is set to 0.11

For COPDgene dataset the method is configured as follows:

  • images are resampled to 1x1x1 mm^3 resolution
  • displacements control point grid spacing: 8x8x8 pixels and are refined to 4x4x4 pixels at the finest pyramid level
  • LCC kernel sigma is 2.1 mm
  • isotropic TV regularization weight is set to 0.11

Compared to "Vishnevskiy V, Gass T, Szekely G, Tanner C, Goksel O. Isotropic total variation regularization of displacements in parametric image registration. IEEE TMI. 2017." the method is improved in the following ways:

  • Image pyramids are constructed with downscaling factor of 0.7 (0.5 in the original paper)
  • Image gradients are computed taking into account image interpolation scheme (we used approximation grad(warp(image)) in the original paper)
  • Bugs is displacement field upsampling were fixed

We also provide registration accuracy using different configurations of the method:

DIR

Method config 4DCT 1 4DCT 2 4DCT 3 4DCT 4 4DCT 5 4DCT 6 4DCT 7 4DCT 8 4DCT 9 4DCT 10 Mean TRE Mean Time (sec.)
cp_refinements = 0
loc_cc_approximate = false
Original resolution
0.82 0.83 0.98 1.28 1.13 0.88 0.83 1.05 0.95 0.87 0.96 72
cp_refinements = 0
loc_cc_approximate = true
Original resolution
0.78 0.79 0.96 1.27 1.09 0.89 0.86 1.07 0.94 0.93 0.96 57
cp_refinements = 0
loc_cc_approximate = true
Resampled to 1x1x1 mm^3
0.77 0.75 0.93 1.26 1.07 0.83 0.80 1.01 0.91 0.84 0.92 130
cp_refinements = 0
loc_cc_approximate = false
Resampled to 1x1x1 mm^3
0.80 0.77 0.92 1.28 1.11 0.81 0.80 1.12 0.90 0.79 0.93 188
cp_refinements = 1
loc_cc_approximate = true
Resampled to 1x1x1 mm^3
0.78 0.74 0.92 1.27 1.09 0.84 0.81 0.99 0.92 0.85 0.92 178
cp_refinements = 1
loc_cc_approximate = false
Resampled to 1x1x1 mm^3
0.80 0.77 0.92 1.30 1.13 0.78 0.79 1.00 0.91 0.82 0.92 300

COPD

Method config COPD 1 COPD 2 COPD 3 COPD 4 COPD 5 COPD 6 COPD 7 COPD 8 COPD 9 COPD 10 Mean TRE Mean Time (sec.)
cp_refinements = 0
loc_cc_approximate = false
Linear interpolation
0.78 3.38 0.79 0.72 0.77 1.00 0.81 1.19 0.67 0.86 1.09 224
cp_refinements = 0
loc_cc_approximate = true
Linear interpolation
0.79 3.81 0.83 0.78 0.98 0.90 0.82 1.02 0.72 1.07 1.17 186
cp_refinements = 1
loc_cc_approximate = false
Linear interpolation
0.72 2.22 0.79 0.68 0.81 0.70 0.81 0.83 0.64 0.86 0.91 359
cp_refinements = 1
loc_cc_approximate = true
Linear interpolation
0.74 3.22 0.79 0.74 0.80 1.13 0.81 0.85 0.66 0.92 1.07 275
cp_refinements = 1
loc_cc_approximate = false
Cubic interpolation
0.71 1.91 0.77 0.67 0.71 0.66 0.75 0.78 0.64 0.85 0.8461 442

Running The Code

The data used in this challenge is publicly available by request from organizers. bpath variable sets the basepath for the dataset. Please double check that you directory formatting agrees with the one used by us: tree output.

pTV

If you decide to use our registration toolbox please consider referring to this repository or the following paper:

@article{vishnevskiy2017isotropic,
  title={Isotropic total variation regularization of displacements in parametric image registration},
  author={Vishnevskiy, Valery and Gass, Tobias and Szekely, Gabor and Tanner, Christine and Goksel, Orcun},
  journal={IEEE transactions on medical imaging},
  volume={36},
  number={2},
  pages={385--395},
  year={2017},
  publisher={IEEE}
}

The code is developed by Valery Vishnevskiy, Cardiac Magnetic Resonance group, Institute for Biomedical Engineering, ETH Zurich, University of Zurich, Computer-assisted Applications in Medicine, ETH Zurich.

ETHZurich ETHZurich