Learning Unsupervised Parameter-Specific Affine Transformation for Medical Images Registration

Chen, Xu, Meng, Yanda, Zhao, Yitian, Williams, Rachel, Vallabhaneni, Srinivasa R and Zheng, Yalin
(2021) Learning Unsupervised Parameter-Specific Affine Transformation for Medical Images Registration. In: MICCAI.

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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 or Workshop 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
Divisions: Faculty of Health and Life Sciences
Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences
Depositing User: Symplectic Admin
Date Deposited: 03 Aug 2021 07:31
Last Modified: 21 Jun 2024 03:56
DOI: 10.1007/978-3-030-87202-1_3
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3132037