Home Publications Research Grants Inventions & Patents Awards Additional Research Activities Faculties & Institutions Research Areas

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

Publication year: 2020

Journal

Publisher: Springer

Pages Range: 301-306

Conference Proceedings Title: Informatik aktuell

Event location: Berlin

ISBN: 9783658292669

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

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 diffcult 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%.

Authors with CRIS profile

Related research project(s)

Involved external institutions

How to cite

APA:

Bayer, S., Zhong, X., Fu, W., Ravikumar, N., & Maier, A. (2020). Imitation learning network for fundus image registration using a divide-and-conquer approach. In Thomas Tolxdorff, Thomas M. Deserno, Heinz Handels, Andreas Maier, Klaus H. Maier-Hein, Christoph Palm (Eds.), Informatik aktuell (pp. 301-306). Berlin, DE: Springer.

MLA:

Bayer, Siming, et al. "Imitation learning network for fundus image registration using a divide-and-conquer approach." Proceedings of the International workshop on Algorithmen - Systeme - Anwendungen, 2020, Berlin Ed. Thomas Tolxdorff, Thomas M. Deserno, Heinz Handels, Andreas Maier, Klaus H. Maier-Hein, Christoph Palm, Springer, 2020. 301-306.

BibTeX: Download