U-Net with spatial pyramid pooling for drusen segmentation in optical coherence tomography

TL;DR

This paper introduces an enhanced U-Net model with spatial pyramid pooling for improved drusen segmentation in OCT images, demonstrating superior performance over standard U-Net on AMD datasets.

Contribution

The study extends U-Net with spatial pyramid pooling to incorporate global context, improving drusen segmentation accuracy in OCT images.

Findings

Proposed model outperforms standard U-Net in segmentation accuracy.

Enhanced network effectively captures global features for better delineation.

Validated on a dataset of 425 OCT scans from AMD patients.

Abstract

The presence of drusen is the main hallmark of early/intermediate age-related macular degeneration (AMD). Therefore, automated drusen segmentation is an important step in image-guided management of AMD. There are two common approaches to drusen segmentation. In the first, the drusen are segmented directly as a binary classification task. In the second approach, the surrounding retinal layers (outer boundary retinal pigment epithelium (OBRPE) and Bruch's membrane (BM)) are segmented and the remaining space between these two layers is extracted as drusen. In this work, we extend the standard U-Net architecture with spatial pyramid pooling components to introduce global feature context. We apply the model to the task of segmenting drusen together with BM and OBRPE. The proposed network was trained and evaluated on a longitudinal OCT dataset of 425 scans from 38 patients with early/intermediate AMD. This preliminary study showed that the proposed network consistently outperformed the standard U-net model.