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A segmentation based deep learning framework for multimodal retinal image registration

Citation Author(s):
Yiqian Wang, Junkang Zhang, Cheolhong An, Melina Cavichini, Mahima Jhingan, Manuel J. Amador-Patarroyo, Christopher P. Long, Dirk-Uwe G. Bartsch, William R. Freeman, Truong Q. Nguyen
Submitted by:
Yiqian Wang
Last updated:
13 May 2020 - 4:42pm
Document Type:
Presentation Slides
Document Year:
2020
Event:
Presenters:
Yiqian Wang
 

Multimodal image registration plays an important role in diagnosing and treating ophthalmologic diseases. In this paper, a deep learning framework for multimodal retinal image registration is proposed. The framework consists of a segmentation network, feature detection and description network, and an outlier rejection network, which focuses only on the globally coarse alignment step using the perspective transformation. We apply the proposed framework to register color fundus images with infrared reflectance images and compare it with the state-of-the-art conventional and learning-based approaches. The proposed framework demonstrates a significant improvement in robustness and accuracy reflected by a higher success rate and Dice coefficient compared to other coarse alignment methods.

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