@article{
 title = {A Framework for Comparison and Evaluation of Nonlinear Intra-Subject Image Registration Algorithms},
 type = {article},
 year = {2007},
 pages = {1-16},
 websites = {http://hdl.handle.net/1926/561},
 id = {bd9910d2-e0fd-3f43-81ab-d63b6cb427ea},
 created = {2015-02-18T08:30:18.000Z},
 file_attached = {false},
 profile_id = {53d1e3c7-2f16-3c81-9a84-dccd45be4841},
 last_modified = {2019-11-08T01:40:41.434Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Urschler2007},
 folder_uuids = {0ec41d70-75f1-4a99-820b-0a83ccc37f54},
 private_publication = {false},
 abstract = {Performance validation of nonlinear registration algorithms is a difficult problem due to the lack of a suitable ground truth in most applications. However, the ill-posed nature of the nonlinear registration problem and the large space of possible solutions makes the quantitative evaluation of algorithms extremely important. We argue that finding a standardized way of performing evaluation and comparing existing and new algorithms currently is more important than inventing novel methods. While there are already existing evaluation frameworks for nonlinear inter-subject brain registration applications, there is still a lack of protocols for intra-subject studies or soft tissue organs. In this work we present such a framework which is designed in an é?éopen-sourceé?é and é?éopen-dataé?é manner around the Insight Segmentation & Registration Toolkit. The goal of our work is to provide the research community with the basis framework that should be extended by interested people in a community effort to gain importance for evaluation studies. We demonstrate our proposed framework on a sample evaluation and release its implementation and associated tools to the public domain.},
 bibtype = {article},
 author = {Urschler, Martin and Kluckner, Stefan and Bischof, Horst},
 journal = {Insight Journal},
 number = {2007 MICCAI Open Science Workshop}
}

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