Unsupervised Deformable Image Registration in a Landmark Scarcity Scenario: Choroid OCTA
Title
Unsupervised Deformable Image Registration in a Landmark Scarcity Scenario: Choroid OCTAAuthor(s)
Date
2022-05Citation
López-Varela, E., Novo, J., Fernández-Vigo, J.I., Moreno-Morillo, F.J., Ortega, M. (2022). Unsupervised Deformable Image Registration in a Landmark Scarcity Scenario: Choroid OCTA. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds) Image Analysis and Processing – ICIAP 2022. ICIAP 2022. Lecture Notes in Computer Science, vol 13231. Springer, Cham. https://doi.org/10.1007/978-3-031-06427-2_8
Abstract
[Abstract]: Recent advances in OCTA allow the imaging of blood flow deeper than the retinal layers at the level of the choriocapillaris (CC), where a pattern of small dark areas represents the absence of flow, called flow voids. The distribution of flow voids can be used as a biomarker to diagnose and monitor the progression of relevant pathologies or the efficacy of applied treatments. A pixel-to-pixel comparison can help to carry out this monitoring effectively, although in order to carry out this comparison, the used images must be perfectly aligned. CC images are characterized by their granularity, presenting numerous and complex local deformations, so a deformable registration is necessary to carry out a reliable comparison. However, CC OCTA images also present a characteristic absence of visually significant anatomical structures. This landmark scarcity hardens drastically the identification of points of interest to achieve an accurate registration. Based on this context, we designed a methodology to accurately perform this deformable registration in this challenging scenario. Hence, we propose a convolutional neural network model trained by unsupervised learning to register images in a real clinical scenario, being obtained at different time instants from patients with central serous chorioretinopathy (CSC) treated with photodynamic therapy. Our methodology produces superior alignment to those achieved with other proven methods, helping to improve the monitoring of the efficacy of photodynamic therapy applied to patients with CSC. Our robust and adaptable methodology can also be exploited in other similar scenarios of complex registrations with anatomical landmark scarcity.
Keywords
Ophthalmology
OCTA imaging
Choriocapillaris
Deformable image registration
Flow voids
Convolutional Neural Networks
OCTA imaging
Choriocapillaris
Deformable image registration
Flow voids
Convolutional Neural Networks
Description
This version of the conference paper has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-031-06427-2_8.
Editor version
Rights
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
ISSN
1611-3349
0302-9743
0302-9743
ISBN
978-3-031-06426-5 978-3-031-06427-2