Random expert sampling for deep learning segmentation of acute ischemic stroke on non-contrast CT.

ABSTRACT:

Background

Outlining acutely infarcted tissue on non-contrast CT is a challenging task for which human inter-reader agreement is limited. We explored two different methods for training a supervised deep learning algorithm: one that used a segmentation defined by majority vote among experts and another that trained randomly on separate individual expert segmentations.

Methods

The data set consisted of 260 non-contrast CT studies in 233 patients with acute ischemic stroke recruited from the multicenter DEFUSE 3 (Endovascular Therapy Following Imaging Evaluation for Ischemic Stroke 3) trial. Additional external validation was performed using 33 patients with matched stroke onset times from the University Hospital Lausanne. A benchmark U-Net was trained on the reference annotati

SUBMITTER: Ostmeier S 

PROVIDER: S-EPMC11291713 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature