Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Voxelmorph++ Going beyond the cranial vault with keypoint supervision
  and multi-channel instance optimisation   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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The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
16
The categories of this paper are: stat
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
24
The categories of this paper are: dg-ga
32
The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org
30
The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
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The categories of this paper are: q-fin
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
8
The categories of this paper are: hep-ex