Text-guided multimodal deep learning in magnetic resonance imaging for spinal structures segmentation and lumbar abnormalities identification.

ABSTRACT:

Background

In the domain of recent lumbar magnetic resonance imaging (MRI) analysis, the limitations of unimodal deep learning (DL) methods for medical imaging have gradually been revealed in that they remain insufficient to model the latent relationships within the textual semantics of category terms. These terms are used to define the research targets in medical imaging analysis tasks. Effective modeling of these relationships requires textual representations. Therefore, we proposed a text-guided multimodal DL method for segmenting 19 spinal structures and identifying 5 lumbar abnormalities on T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI) MRI scans.

Methods

We employed ConvNeXt V2 as the image encoder (pretrained on 1,975 unlabeled lumbar MRI scans) and the Con

SUBMITTER: Liu J 

PROVIDER: S-EPMC12514601 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature