Persistent URL of this record https://hdl.handle.net/1887/3243762
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Supervised learning in medical image registration
Predicting registration error can be useful for evaluation of registration procedures, which is important for the adoption of registration techniques in the clinic. In addition, quantitative error prediction can be helpful in improving the registration quality. In this thesis, we proposed two...Show moreImage registration is the process of aligning images by finding the spatial relation between the images. Assuming two images called fixed and moving images are taken at different time, different spatial location, or via a different imaging technique, the aim of image registration is to find an optimal transformation that aligns the fixed and the moving images. Performing an automatic fast image registration with less manual finetuning can speed up numerous medical image processing procedures. In addition, an automatic quality assessment of registration can speed up this time-consuming task. In this thesis, we developed a fast learning-based image registration technique called RegNet.
Predicting registration error can be useful for evaluation of registration procedures, which is important for the adoption of registration techniques in the clinic. In addition, quantitative error prediction can be helpful in improving the registration quality. In this thesis, we proposed two quality assessment mechanisms using random forests (RF) and convolutional long short term memory (ConvLSTM), in which the latter performs faster and more accurate.
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- All authors
- Sokooti, H.
- Supervisor
- Lelieveldt, B.P.F.
- Co-supervisor
- Staring, M.
- Committee
- Schnabel, J.A.; Batenburg, J.; Spruit, M.; Rombouts, S.A.R.B.
- Qualification
- Doctor (dr.)
- Awarding Institution
- Faculty of Medicine, Leiden University Medical Center (LUMC) , Leiden University
- Date
- 2021-11-22
- Title of host publication
- ASCI dissertation series
- ISBN
- 9789492597830
Publication Series
- Name
- 429