Survey of Non-Rigid Registration Tools in Medicine
By András P. Keszei, Benjamin Berkels, Thomas Martin Deserno. 2017
68 citations recorded by the source corpus.
We catalogue available software solutions for non-rigid image registration to support scientists in selecting suitable tools for specific medical registration purposes. Registration tools were identified using non-systematic search in Pubmed, Web of Science, IEEE Xplore® Digital Library, Google Scholar, and through references in identified sources (n = 22). Exclusions are due to unavailability or inappropriateness. The remaining (n = 18) tools were classified by (i) access and technology, (ii) interfaces and application, (iii) living community, (iv) supported file formats, and (v) types of registration methodologies emphasizing the similarity measures implemented. Out of the 18 tools, (i) 12 are open source, 8 are released under a permissive free license, which imposes the least restrictions on the use and further development of the tool, 8 provide graphical processing unit (GPU) support; (ii) 7 are built on software platforms, 5 were developed for brain image registration; (iii) 6 are under active development but only 3 have had their last update in 2015 or 2016; (iv) 16 support the Analyze format, while 7 file formats can be read with only one of the tools; and (v) 6 provide multiple registration methods and 6 provide landmark-based registration methods. Based on open source, licensing, GPU support, active community, several file formats, algorithms, and similarity measures, the tools Elastics and Plastimatch are chosen for the platform ITK and without platform requirements, respectively. Researchers in medical image analysis already have a large choice of registration tools freely available. However, the most recently published algorithms may not be included in the tools, yet.
Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging
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Citations in the corpus
- An overview of deep learning in medical imaging focusing on MRI (2018, methodology): “on correction, real-time MR physics is also an important part of the picture [224, 225]. A wide range of methodological approaches have been developed and tested for various organs and applications40 [229, 230, 231, 232, 233, 234, 235, 236, 237, 238], including \previous generation" articial ne”