Training Deep Learning Models to Work on Multiple Devices by Cross-Domain Learning with No Additional Annotations.

ABSTRACT:

Purpose

To create an unsupervised cross-domain segmentation algorithm for segmenting intraretinal fluid and retinal layers on normal and pathologic macular OCT images from different manufacturers and camera devices.

Design

We sought to use generative adversarial networks (GANs) to generalize a segmentation model trained on one OCT device to segment B-scans obtained from a different OCT device manufacturer in a fully unsupervised approach without labeled data from the latter manufacturer.

Participants

A total of 732 OCT B-scans from 4 different OCT devices (Heidelberg Spectralis, Topcon 1000, Maestro2, and Zeiss Plex Elite 9000).

Methods

We developed an unsupervised GAN model, GANSeg, to segment 7 retinal layers and intraretinal fluid in Topcon 1000 OCT images (

SUBMITTER: Wu Y 

PROVIDER: S-EPMC9868052 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature