Deep learning-based structure segmentation and intramuscular fat annotation on lumbar magnetic resonance imaging.

ABSTRACT:

Background

Lumbar disc herniation (LDH) is a prevalent cause of low back pain. LDH patients commonly experience paraspinal muscle atrophy and fatty infiltration (FI), which further exacerbates the symptoms of low back pain. Magnetic resonance imaging (MRI) is crucial for assessing paraspinal muscle condition. Our study aims to develop a dual-model for automated muscle segmentation and FI annotation on MRI, assisting clinicians evaluate LDH conditions comprehensively.

Methods

The study retrospectively collected data diagnosed with LDH from December 2020 to May 2022. The dataset was split into a 7:3 ratio for training and testing, with an external test set prepared to validate model generalizability. The model's performance was evaluated using average precision (AP), recall an

SUBMITTER: Xu Y 

PROVIDER: S-EPMC11406510 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature