Recurrence With Correlation Network for Medical Image Registration

• URL: http://arxiv.org/abs/2302.02283v1
• Date: Sun, 5 Feb 2023 02:41:46 GMT
• Title: Recurrence With Correlation Network for Medical Image Registration
• Authors: Vignesh Sivan, Teodora Vujovic, Raj Ranabhat, Alexander Wong, Stewart Mclachlin, Michael Hardisty
• Abstract summary: We present Recurrence with Correlation Network (RWCNet), a medical image registration network with multi-scale features and a cost volume layer. We demonstrate that these architectural features improve medical image registration accuracy in two image registration datasets.
• Score: 66.63200823918429
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present Recurrence with Correlation Network (RWCNet), a medical image registration network with multi-scale features and a cost volume layer. We demonstrate that these architectural features improve medical image registration accuracy in two image registration datasets prepared for the MICCAI 2022 Learn2Reg Workshop Challenge. On the large-displacement National Lung Screening Test (NLST) dataset, RWCNet is able to achieve a total registration error (TRE) of 2.11mm between corresponding keypoints without instance fine-tuning. On the OASIS brain MRI dataset, RWCNet is able to achieve an average dice overlap of 81.7% for 35 different anatomical labels. It outperforms another multi-scale network, the Laplacian Image Registration Network (LapIRN), on both datasets. Ablation experiments are performed to highlight the contribution of the various architectural features. While multi-scale features improved validation accuracy for both datasets, the cost volume layer and number of recurrent steps only improved performance on the large-displacement NLST dataset. This result suggests that cost volume layer and iterative refinement using RNN provide good support for optimization and generalization in large-displacement medical image registration. The code for RWCNet is available at https://github.com/vigsivan/optimization-based-registration.

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