Joint affine and deformable three‐dimensional networks for brain MRI registration

Autor: Di Lin, Chenchen Qin, Zhenyu Zhu, Dong Ni, Yi Rao, Yiqin Cao, Qi Dou, Yi Wang
Rok vydání: 2021
Předmět:
Zdroj: Medical Physics. 48:1182-1196
ISSN: 2473-4209
0094-2405
DOI: 10.1002/mp.14674
Popis: Purpose Volumetric medical image registration has important clinical significance. Traditional registration methods may be time-consuming when processing large volumetric data due to their iterative optimizations. In contrast, existing deep learning-based networks can obtain the registration quickly. However, most of them require independent rigid alignment before deformable registration; these two steps are often performed separately and cannot be end-to-end. Methods We propose an end-to-end joint affine and deformable network for three-dimensional (3D) medical image registration. The proposed network combines two deformation methods; the first one is for obtaining affine alignment and the second one is a deformable subnetwork for achieving the nonrigid registration. The parameters of the two subnetworks are shared. The global and local similarity measures are used as loss functions for the two subnetworks, respectively. Moreover, an anatomical similarity loss is devised to weakly supervise the training of the whole registration network. Finally, the trained network can perform deformable registration in one forward pass. Results The efficacy of our network was extensively evaluated on three public brain MRI datasets including Mindboggle101, LPBA40, and IXI. Experimental results demonstrate our network consistently outperformed several state-of-the-art methods with respect to the metrics of Dice index (DSC), Hausdorff distance (HD), and average symmetric surface distance (ASSD). Conclusions The proposed network provides accurate and robust volumetric registration without any pre-alignment requirement, which facilitates the end-to-end deformable registration.
Databáze: OpenAIRE
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