Open Access

ARTICLE

Optical Flow with Learning Feature for Deformable Medical Image Registration

Jinrong Hu¹, Lujin Li¹, Ying Fu¹, Maoyang Zou¹, Jiliu Zhou¹, Shanhui Sun^2,*

1 Department of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
2 Curacloud Corporation, 999 Third Ave, Suite 700 Seattle, WA, 98104, USA

* Corresponding Author: Shanhui Sun. Email:

Computers, Materials & Continua 2022, 71(2), 2773-2788. https://doi.org/10.32604/cmc.2022.017916

Received 17 February 2021; Accepted 13 April 2021; Issue published 07 December 2021

Abstract

Deformable medical image registration plays a vital role in medical image applications, such as placing different temporal images at the same time point or different modality images into the same coordinate system. Various strategies have been developed to satisfy the increasing needs of deformable medical image registration. One popular registration method is estimating the displacement field by computing the optical flow between two images. The motion field (flow field) is computed based on either gray-value or handcrafted descriptors such as the scale-invariant feature transform (SIFT). These methods assume that illumination is constant between images. However, medical images may not always satisfy this assumption. In this study, we propose a metric learning-based motion estimation method called Siamese Flow for deformable medical image registration. We train metric learners using a Siamese network, which produces an image patch descriptor that guarantees a smaller feature distance in two similar anatomical structures and a larger feature distance in two dissimilar anatomical structures. In the proposed registration framework, the flow field is computed based on such features and is close to the real deformation field due to the excellent feature representation ability of the Siamese network. Experimental results demonstrate that the proposed method outperforms the Demons, SIFT Flow, Elastix, and VoxelMorph networks regarding registration accuracy and robustness, particularly with large deformations.

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Keywords

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