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Unsupervised Learning Model for Registration of Multi-phase Ultra-Widefield Fluorescein Angiography

Title
Unsupervised Learning Model for Registration of Multi-phase Ultra-Widefield Fluorescein Angiography
Authors
Lee, Gyoeng MinSeo, Kwang DeokSong, Hye JuPark, Dong GeunRyu, Ga HyungSagong, MinPark, Sang Hyun
DGIST Authors
Park, Sang Hyun
Issue Date
2020-10-05
Citation
International Conference on Medical Image Computing and Computer Assisted Interventions, 201-210
Type
Conference
ISBN
9783030597153
ISSN
0302-9743
Abstract
Registration methods based on unsupervised deep learning have achieved good performances, but are often ineffective on the registration of inhomogeneous images containing large displacements. In this paper, we propose an unsupervised learning-based registration method that effectively aligns multi-phase Ultra-Widefield (UWF) fluorescein angiography (FA) retinal images acquired over the time after a contrast agent is applied to the eye. The proposed method consists of an encoder-decoder style network for predicting displacements and spatial transformers to create moved images using the predicted displacements. Unlike existing methods, we transform the moving image as well as its vesselness map through the spatial transformers, and then compute the loss by comparing them with the target image and the corresponding maps. To effectively predict large displacements, displacement maps are estimated at multiple levels of a decoder and the losses computed from the maps are used in optimization. For evaluation, experiments were performed on 64 pairs of early- and late-phase UWF retinal images. Experimental results show that the proposed method outperforms the existing methods. © 2020, Springer Nature Switzerland AG.
URI
http://hdl.handle.net/20.500.11750/12874
DOI
10.1007/978-3-030-59716-0_20
Publisher
Springer Science and Business Media Deutschland GmbH
Related Researcher
  • Author Park, Sang Hyun Medical Image & Signal Processing Lab
  • Research Interests 컴퓨터비전, 인공지능, 의료영상처리
Files:
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Collection:
Department of Robotics and Mechatronics EngineeringMedical Image & Signal Processing Lab2. Conference Papers


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