Analyzing an Imitation Learning Network for Fundus Image Registration Using a Divide-and-Conquer Approach

Bayer S, Zhong X, Fu W, Ravikumar N, Maier A (2020)

Publication Type: Conference contribution, Conference Contribution

Publication year: 2020

Publisher: Sringer

Conference Proceedings Title: Algorithmen – Systeme – Anwendungen. Proceedings des Workshops

Event location: Berlin

DOI: 10.1007/978-3-658-29267-6_67

Open Access Link: https://www.researchgate.net/publication/338047089_Analyzing_an_Imitation_Learning_Network_for_Fundus_Image_Registration_Using_a_Divide-and-Conquer_Approach?_sg=PVTxQ6PPa_u6unIko2fDziwgeAsG5HU-q5IEbTX0WNp-wF8rjJUTBNfSjseERn-_H_CuFbbS6FnL-A.nqfF6h6q2-

Abstract

Comparison of microvascular circulation on fundoscopic images is a non-invasive clinical indication for the diagnosis and monitoring of diseases, such as diabetes and hypertensions. The differences between intra-patient images can be assessed quantitatively by registering serial acquisitions. Due to the variability of the images (i.e. contrast, luminosity) and the anatomical changes of the retina, the registration of fundus images remains a challenging task.
Recently, several deep learning approaches have been proposed to register fundus images in an end-to-end fashion, achieving remarkable results.However, the results are difficult to interpret and analyze. In this work, we propose an imitation learning framework for the registration of 2D color funduscopic images for a wide range of applications such as disease monitoring, image stitching and super-resolution. We follow a divide-and-conquer approach to improve the interpretability of the proposed network, and analyze both the influence of the input image and the hyperparameters on the registration result.The results show that the proposed registration network reduces the initial target registration error up to 95 %

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How to cite

APA:

Bayer, S., Zhong, X., Fu, W., Ravikumar, N., & Maier, A. (2020). Analyzing an Imitation Learning Network for Fundus Image Registration Using a Divide-and-Conquer Approach. In Springer (Eds.), Algorithmen – Systeme – Anwendungen. Proceedings des Workshops. Berlin, DE: Sringer.

MLA:

Bayer, Siming, et al. "Analyzing an Imitation Learning Network for Fundus Image Registration Using a Divide-and-Conquer Approach." Proceedings of the Bildgebung für die Medizin 2020, Berlin Ed. Springer, Sringer, 2020.

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