Deep learning-based detection and segmentation of diffusion abnormalities in acute ischemic stroke.

ABSTRACT:

Background

Accessible tools to efficiently detect and segment diffusion abnormalities in acute strokes are highly anticipated by the clinical and research communities.

Methods

We developed a tool with deep learning networks trained and tested on a large dataset of 2,348 clinical diffusion weighted MRIs of patients with acute and sub-acute ischemic strokes, and further tested for generalization on 280 MRIs of an external dataset (STIR).

Results

Our proposed model outperforms generic networks and DeepMedic, particularly in small lesions, with lower false positive rate, balanced precision and sensitivity, and robustness to data perturbs (e.g., artefacts, low resolution, technical heterogeneity). The agreement with human delineation rivals the inter-evaluator agreement; t

SUBMITTER: Liu CF 

PROVIDER: S-EPMC9053217 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature