Learning Unsupervised Parameter-Specific Affine Transformation for Medical Images Registration

Chen, Xu, Meng, Yanda ORCID: 0000-0001-7344-2174, Zhao, Yitian, Williams, Rachel ORCID: 0000-0002-1954-0256, Vallabhaneni, Srinivasa R and Zheng, Yalin ORCID: 0000-0002-7873-0922 (2021) Learning Unsupervised Parameter-Specific Affine Transformation for Medical Images Registration In: MICCAI.

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Abstract

Affine registration has recently been formulated using deep learning frameworks to establish spatial correspondences between different images. In this work, we propose a new unsupervised model that investigates two new strategies to tackle fundamental problems related to affine registration. More specifically, the new model 1) has the advantage to explicitly learn specific geometric transformation parameters (e.g. translations, rotation, scaling and shearing); and 2) can effectively understand the context between the images via cross-stitch units allowing feature exchange. The proposed model is evaluated on two two-dimensional X-ray datasets and a three-dimensional CT dataset. Our experimental results show that our model not only outperforms state-of-art approaches and also can predict specific transformation parameters. Our core source code is made available online ¹ (¹ https://github.com/xuuuuuuchen/PASTA ).

Item Type:	Conference Item (Unspecified)
Uncontrolled Keywords:	46 Information and Computing Sciences, 4603 Computer Vision and Multimedia Computation, Networking and Information Technology R&D (NITRD), Machine Learning and Artificial Intelligence, Bioengineering
Divisions:	Faculty of Health & Life Sciences Faculty of Health & Life Sciences > Inst. Life Courses & Medical Sciences
Depositing User:	Symplectic Admin
Date Deposited:	03 Aug 2021 07:31
Last Modified:	11 Sep 2026 04:56
DOI:	10.1007/978-3-030-87202-1_3
Related Websites:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3132037
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