Drusen segmentation with sparse volumetric SD-OCT sampling

Abum Okemgbo; I. David Rein; Yiyang Wang; Amani Fawzi; Jacob Furst; Daniela Raicu

doi:10.1117/12.2581116

15 February 2021 Drusen segmentation with sparse volumetric SD-OCT sampling

Abum Okemgbo, I. David Rein, Yiyang Wang, Amani Fawzi, Jacob Furst, Daniela Raicu

Proceedings Volume 11596, Medical Imaging 2021: Image Processing; 115962Z (2021) https://doi.org/10.1117/12.2581116
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

Age-Related Macular Degeneration (AMD) is a common eye disease characterized by the build-up of drusen, small deposits of extracellular materials in the macula. Early detection of drusen is key to understanding the progression of AMD. Therefore, accurate and robust segmentation of drusen during AMD progression is important for automated detection, classification, diagnosis, and prognosis tasks. Spectral-domain optical coherence tomography (SD-OCT) is a popular macular imaging modality used for these tasks. However, because of the trade-off between resolution and speed, often clinical OCT scans will contain far fewer images per volume than the 100-200 images the drusen segmentation literature generally utilizes. To address this disparity, we develop a novel drusen segmentation algorithm for SD-OCT volumes with low volumetric resolution. We achieve comparable results to similar work, while using on average 16% of the volumetric information. We evaluate our segmentation approach on manually segmented images by two graders, and achieve median Dice coefficient scores of 0.75 and 0.66, respectively, which are close to our median inter-reader variability score of 0.75.

Conference Presentation

Citation Download Citation

Abum Okemgbo, I. David Rein, Yiyang Wang, Amani Fawzi, Jacob Furst, and Daniela Raicu "Drusen segmentation with sparse volumetric SD-OCT sampling", Proc. SPIE 11596, Medical Imaging 2021: Image Processing, 115962Z (15 February 2021); https://doi.org/10.1117/12.2581116

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