F3RNet: Full-Resolution Residual Registration Network for Deformable Image Registration

• URL: http://arxiv.org/abs/2009.07151v3
• Date: Mon, 7 Dec 2020 03:08:38 GMT
• Title: F3RNet: Full-Resolution Residual Registration Network for Deformable Image Registration
• Authors: Zhe Xu, Jie Luo, Jiangpeng Yan, Xiu Li, Jagadeesan Jayender
• Abstract summary: Deformable image registration (DIR) is essential for many image-guided therapies. We propose a novel unsupervised registration network, namely the Full-Resolution Residual Registration Network (F3RNet) One stream takes advantage of the full-resolution information that facilitates accurate voxel-level registration. The other stream learns the deep multi-scale residual representations to obtain robust recognition.
• Score: 21.99118499516863
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deformable image registration (DIR) is essential for many image-guided therapies. Recently, deep learning approaches have gained substantial popularity and success in DIR. Most deep learning approaches use the so-called mono-stream "high-to-low, low-to-high" network structure, and can achieve satisfactory overall registration results. However, accurate alignments for some severely deformed local regions, which are crucial for pinpointing surgical targets, are often overlooked. Consequently, these approaches are not sensitive to some hard-to-align regions, e.g., intra-patient registration of deformed liver lobes. In this paper, we propose a novel unsupervised registration network, namely the Full-Resolution Residual Registration Network (F3RNet), for deformable registration of severely deformed organs. The proposed method combines two parallel processing streams in a residual learning fashion. One stream takes advantage of the full-resolution information that facilitates accurate voxel-level registration. The other stream learns the deep multi-scale residual representations to obtain robust recognition. We also factorize the 3D convolution to reduce the training parameters and enhance network efficiency. We validate the proposed method on a clinically acquired intra-patient abdominal CT-MRI dataset and a public inspiratory and expiratory thorax CT dataset. Experiments on both multimodal and unimodal registration demonstrate promising results compared to state-of-the-art approaches.

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