Contrast quality control for segmentation task based on deep learning models-Application to stroke lesion in CT imaging.

ABSTRACT:

Introduction

Although medical imaging plays a crucial role in stroke management, machine learning (ML) has been increasingly used in this field, particularly in lesion segmentation. Despite advances in acquisition technologies and segmentation architectures, one of the main challenges of subacute stroke lesion segmentation in computed tomography (CT) imaging is image contrast.

Methods

To address this issue, we propose a method to assess the contrast quality of an image dataset with a ML trained model for segmentation. This method identifies the critical contrast level below which the medical-imaging model fails to learn meaningful content from images. Contrast measurement relies on the Fisher's ratio, estimating how well the stroke lesion is contrasted from the background. T

SUBMITTER: Moreau J 

PROVIDER: S-EPMC11849432 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature