@inproceedings{
 title = {Robust Optical Flow Based Deformable Registration of Thoracic CT Images},
 type = {inproceedings},
 year = {2010},
 pages = {195-204},
 city = {Beijing, CN},
 id = {efe83c53-e788-32b1-9ddd-4ecfa821f3f5},
 created = {2015-03-13T13:28:18.000Z},
 file_attached = {false},
 profile_id = {53d1e3c7-2f16-3c81-9a84-dccd45be4841},
 last_modified = {2019-11-08T01:40:33.164Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Urschler2010_MICCAIWS},
 notes = {Poster},
 folder_uuids = {0ec41d70-75f1-4a99-820b-0a83ccc37f54},
 private_publication = {false},
 abstract = {We present an optical flow deformable registration method which is based on robust measures for data and regularization terms. We show two specific implementations of the method, where one penalizes gradients in the displacement field in an isotropic fashion and the other one regularizes by weighting the penalization according to the image gradients anisotropically. Our data term consists of the L 1-norm of the standard optical flow constraint. We show a numerical algorithm that solves the two proposed models in a primal-dual optimization setup. Our algorithm works in a multi-resolution manner and it is applied to the 20 data sets of the EMPIRE10 registration challenge. Our results show room for improvement. Our rather simple model does not penalize non-diffeomorphic transformations, which leads to bad results on one of the evaluation measures, and it seems unsuited for large deformations cases. However, our algorithm is able to perform registrations of data set sizes around 400 3 on the order of a few minutes using a dedicated CUDA based GPU implementation, which is very fast compared to other reported algorithms.},
 bibtype = {inproceedings},
 author = {Urschler, Martin and Werlberger, Manuel and Scheurer, Eva and Bischof, Horst},
 booktitle = {MICCAI Workshop Medical Image Analysis in the Clinic: A Grand Challenge}
}

{"_id":"qZXPdG33rZLueLePK","bibbaseid":"urschler-werlberger-scheurer-bischof-robustopticalflowbaseddeformableregistrationofthoracicctimages-2010","authorIDs":["5dc49ed5d87d2edf0100002b","5deeebf3dca291de01000036","5e11339e495520de0100002e","5e2f20334557f3de01000117","5e3228cd7b9658f20100003b","7Kn6pNXYAbwfP2qf9","GNRNX8XY6YsvfwLoT","QLJrXtcicsqgMiwYa","R2nAkk8csQ8oKb5gj","Ww5ofBLQKYbrF3J2m","dPNz6LJCS62XHAwue","uhYMbdfHmE55Dkgw2","vQb2TpF7F83goeATt"],"author_short":["Urschler, M.","Werlberger, M.","Scheurer, E.","Bischof, H."],"bibdata":{"title":"Robust Optical Flow Based Deformable Registration of Thoracic CT Images","type":"inproceedings","year":"2010","pages":"195-204","city":"Beijing, CN","id":"efe83c53-e788-32b1-9ddd-4ecfa821f3f5","created":"2015-03-13T13:28:18.000Z","file_attached":false,"profile_id":"53d1e3c7-2f16-3c81-9a84-dccd45be4841","last_modified":"2019-11-08T01:40:33.164Z","read":false,"starred":false,"authored":"true","confirmed":"true","hidden":false,"citation_key":"Urschler2010_MICCAIWS","notes":"Poster","folder_uuids":"0ec41d70-75f1-4a99-820b-0a83ccc37f54","private_publication":false,"abstract":"We present an optical flow deformable registration method which is based on robust measures for data and regularization terms. We show two specific implementations of the method, where one penalizes gradients in the displacement field in an isotropic fashion and the other one regularizes by weighting the penalization according to the image gradients anisotropically. Our data term consists of the L 1-norm of the standard optical flow constraint. We show a numerical algorithm that solves the two proposed models in a primal-dual optimization setup. Our algorithm works in a multi-resolution manner and it is applied to the 20 data sets of the EMPIRE10 registration challenge. Our results show room for improvement. Our rather simple model does not penalize non-diffeomorphic transformations, which leads to bad results on one of the evaluation measures, and it seems unsuited for large deformations cases. However, our algorithm is able to perform registrations of data set sizes around 400 3 on the order of a few minutes using a dedicated CUDA based GPU implementation, which is very fast compared to other reported algorithms.","bibtype":"inproceedings","author":"Urschler, Martin and Werlberger, Manuel and Scheurer, Eva and Bischof, Horst","booktitle":"MICCAI Workshop Medical Image Analysis in the Clinic: A Grand Challenge","bibtex":"@inproceedings{\n title = {Robust Optical Flow Based Deformable Registration of Thoracic CT Images},\n type = {inproceedings},\n year = {2010},\n pages = {195-204},\n city = {Beijing, CN},\n id = {efe83c53-e788-32b1-9ddd-4ecfa821f3f5},\n created = {2015-03-13T13:28:18.000Z},\n file_attached = {false},\n profile_id = {53d1e3c7-2f16-3c81-9a84-dccd45be4841},\n last_modified = {2019-11-08T01:40:33.164Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Urschler2010_MICCAIWS},\n notes = {Poster},\n folder_uuids = {0ec41d70-75f1-4a99-820b-0a83ccc37f54},\n private_publication = {false},\n abstract = {We present an optical flow deformable registration method which is based on robust measures for data and regularization terms. We show two specific implementations of the method, where one penalizes gradients in the displacement field in an isotropic fashion and the other one regularizes by weighting the penalization according to the image gradients anisotropically. Our data term consists of the L 1-norm of the standard optical flow constraint. We show a numerical algorithm that solves the two proposed models in a primal-dual optimization setup. Our algorithm works in a multi-resolution manner and it is applied to the 20 data sets of the EMPIRE10 registration challenge. Our results show room for improvement. Our rather simple model does not penalize non-diffeomorphic transformations, which leads to bad results on one of the evaluation measures, and it seems unsuited for large deformations cases. However, our algorithm is able to perform registrations of data set sizes around 400 3 on the order of a few minutes using a dedicated CUDA based GPU implementation, which is very fast compared to other reported algorithms.},\n bibtype = {inproceedings},\n author = {Urschler, Martin and Werlberger, Manuel and Scheurer, Eva and Bischof, Horst},\n booktitle = {MICCAI Workshop Medical Image Analysis in the Clinic: A Grand Challenge}\n}","author_short":["Urschler, M.","Werlberger, M.","Scheurer, E.","Bischof, H."],"biburl":"https://bibbase.org/service/mendeley/53d1e3c7-2f16-3c81-9a84-dccd45be4841","bibbaseid":"urschler-werlberger-scheurer-bischof-robustopticalflowbaseddeformableregistrationofthoracicctimages-2010","role":"author","urls":{},"metadata":{"authorlinks":{"urschler, m":"https://martinurschler.blogs.auckland.ac.nz/publications/"}},"downloads":0},"bibtype":"inproceedings","creationDate":"2019-11-07T22:46:45.695Z","downloads":0,"keywords":[],"search_terms":["robust","optical","flow","based","deformable","registration","thoracic","images","urschler","werlberger","scheurer","bischof"],"title":"Robust Optical Flow Based Deformable Registration of Thoracic CT Images","year":2010,"biburl":"https://bibbase.org/service/mendeley/53d1e3c7-2f16-3c81-9a84-dccd45be4841","dataSources":["6jn29mijTw27iZTdN","ya2CyA73rpZseyrZ8","2252seNhipfTmjEBQ"]}