3D deformable registration of longitudinal abdominopelvic CT images using unsupervised deep learning
Maureen van Eijnatten; Leonardo Rundo; K. Joost Batenburg; Felix Lucka; Emma Beddowes; Carlos Caldas; Ferdia A. Gallagher; Evis Sala · 2020 · arXiv
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract (excerpt)
This study investigates the use of the unsupervised deep learning framework VoxelMorph for deformable registration of longitudinal abdominopelvic CT images acquired in patients with bone metastases from breast cancer. The CT images were refined prior to registration by automatically removing the CT table and all other extra-corporeal components. To improve the learning capabilities of VoxelMorph when only a limited amount of training data is available, a novel incremental training strategy is proposed based on simulated deformations of consecutive CT images. In a 4-fold cross-validation scheme
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Metadata source: arXiv