Abstract
In medical imaging, deep learning has been applied to segmentation and classification tasks successfully, whereas its use for image registration tasks is still limited. The use of discrete registration can alleviate the problems limiting the use of CNN based registration for large displacements by helping to capture more complex deformations. We evaluate different building blocks of learning based discrete registration for the CuRIOUS multimodal image registration challenge. We also propose a new attention module, which estimates information contents of a grid point, compare different loss functions and evaluate the influence of self-supervised pre-training of feature extraction step.
This work is funded by the German Research Foundation DFG (HE 7364/2).
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Ha, I.Y., Heinrich, M.P. (2019). Comparing Deep Learning Strategies and Attention Mechanisms of Discrete Registration for Multimodal Image-Guided Interventions. In: Zhou, L., et al. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention. LABELS HAL-MICCAI CuRIOUS 2019 2019 2019. Lecture Notes in Computer Science(), vol 11851. Springer, Cham. https://doi.org/10.1007/978-3-030-33642-4_16
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DOI: https://doi.org/10.1007/978-3-030-33642-4_16
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