Feasibility of Automated Segmentation of Pigmented Choroidal Lesions in OCT Data With Deep Learning.

ABSTRACT:

Purpose

To evaluate the feasibility of automated segmentation of pigmented choroidal lesions (PCLs) in optical coherence tomography (OCT) data and compare the performance of different deep neural networks.

Methods

Swept-source OCT image volumes were annotated pixel-wise for PCLs and background. Three deep neural network architectures were applied to the data: the multi-dimensional gated recurrent units (MD-GRU), the V-Net, and the nnU-Net. The nnU-Net was used to compare the performance of two-dimensional (2D) versus three-dimensional (3D) predictions.

Results

A total of 121 OCT volumes were analyzed (100 normal and 21 PCLs). Automated PCL segmentations were successful with all neural networks. The 3D nnU-Net predictions showed the highest recall with a mean of 0.77 ±

SUBMITTER: Valmaggia P 

PROVIDER: S-EPMC9526362 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature