Voxelmorph++ Going beyond the cranial vault with keypoint supervision and multi-channel instance optimisation

• URL: http://arxiv.org/abs/2203.00046v1
• Date: Mon, 28 Feb 2022 19:23:29 GMT
• Title: Voxelmorph++ Going beyond the cranial vault with keypoint supervision and multi-channel instance optimisation
• Authors: Mattias P. Heinrich and Lasse Hansen
• Abstract summary: Recent Learn2Reg benchmark shows single-scale U-Net architectures fall short of state-of-the-art performance for abdominal or intra-patient lung registration. Here, we propose two straightforward steps that greatly reduce this gap in accuracy. First, we employ keypoint self-supervision with a novel network head that predicts a discretised heatmap. Second, we replace multiple learned fine-tuning steps by a single instance with hand-crafted features and the Adam optimiser.
• Score: 8.88841928746097
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The majority of current research in deep learning based image registration addresses inter-patient brain registration with moderate deformation magnitudes. The recent Learn2Reg medical registration benchmark has demonstrated that single-scale U-Net architectures, such as VoxelMorph that directly employ a spatial transformer loss, often do not generalise well beyond the cranial vault and fall short of state-of-the-art performance for abdominal or intra-patient lung registration. Here, we propose two straightforward steps that greatly reduce this gap in accuracy. First, we employ keypoint self-supervision with a novel network head that predicts a discretised heatmap and robustly reduces large deformations for better robustness. Second, we replace multiple learned fine-tuning steps by a single instance optimisation with hand-crafted features and the Adam optimiser. Different to other related work, including FlowNet or PDD-Net, our approach does not require a fully discretised architecture with correlation layer. Our ablation study demonstrates the importance of keypoints in both self-supervised and unsupervised (using only a MIND metric) settings. On a multi-centric inspiration-exhale lung CT dataset, including very challenging COPD scans, our method outperforms VoxelMorph by improving nonlinear alignment by 77% compared to 19% - reaching target registration errors of 2 mm that outperform all but one learning methods published to date. Extending the method to semantic features sets new stat-of-the-art performance on inter-subject abdominal CT registration.

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