Fused Detection of Retinal Biomarkers in OCT Volumes

Kurmann, Thomas Kevin; Márquez-Neila, Pablo; Yu, Siqing; Munk, Marion; Wolf, Sebastian; Sznitman, Raphael (2019). Fused Detection of Retinal Biomarkers in OCT Volumes. In: Shen, Dinggang; Liu, Tianming; Peters, Terry M.; Staib, Lawrence H.; Essert, Caroline; Zhou, Sean; Yap, Pew-Thian; Khan, Ali (eds.) MICCAI 2019. Lecture Notes in Computer Science: Vol. 11764 (pp. 255-263). Cham: Springer International Publishing 10.1007/978-3-030-32239-7_29

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Optical Coherence Tomography (OCT) is the primary imaging modality for detecting pathological biomarkers associated to retinal diseases such as Age-Related Macular Degeneration. In practice, clinical diagnosis and treatment strategies are closely linked to biomarkers visible in OCT volumes and the ability to identify these plays an important role in the development of ophthalmic pharmaceutical products. In this context, we present a method that automatically predicts the presence of biomarkers in OCT cross-sections by incorporating information from the entire volume. We do so by adding a bidirectional LSTM to fuse the outputs of a Convolutional Neural Network that predicts individual biomarkers. We thus avoid the need to use pixel-wise annotations to train our method and instead provide fine-grained biomarker information regardless. On a dataset of 416 volumes, we show that our approach imposes coherence between biomarker predictions across volume slices and our predictions are superior to several existing approaches.

Item Type:

Conference or Workshop Item (Paper)

Division/Institute:

10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research
04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Ophthalmology
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - Image Guided Therapy > ARTORG Center - Ophthalmic Technology Lab

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

Kurmann, Thomas Kevin; Márquez Neila, Pablo; Yu, Siqing; Munk, Marion; Wolf, Sebastian and Sznitman, Raphael

Subjects:

500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health
000 Computer science, knowledge & systems
600 Technology > 620 Engineering

ISBN:

978-3-030-32239-7

Series:

Lecture Notes in Computer Science

Publisher:

Springer International Publishing

Language:

English

Submitter:

Thomas Kevin Kurmann

Date Deposited:

25 Oct 2019 15:26

Last Modified:

25 Oct 2019 15:26

Publisher DOI:

10.1007/978-3-030-32239-7_29

BORIS DOI:

10.7892/boris.134198

URI:

https://boris.unibe.ch/id/eprint/134198

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