Learning With Fewer Images via Image Clustering: Application to Intravascular OCT Image Segmentation.

ABSTRACT: Deep learning based methods are routinely used to segment various structures of interest in varied medical imaging modalities. Acquiring annotations for a large number of images requires a skilled analyst, and the process is both time consuming and challenging. Our approach to reduce effort is to reduce the number of images needing detailed annotation. For intravascular optical coherence tomography (IVOCT) image pullbacks, we tested 10% to 100% of training images derived from two schemes: equally-spaced image subsampling and deep-learning- based image clustering. The first strategy involves selecting images at equally spaced intervals from the volume, accounting for the high spatial correlation between neighboring images. In clustering, we used an autoencoder to create a deep feature space

SUBMITTER: Kolluru C 

PROVIDER: S-EPMC8023588 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature