An intensity-based self-supervised domain adaptation method for intervertebral disc segmentation in magnetic resonance imaging.

ABSTRACT:

Background and objective

Accurate IVD segmentation is crucial for diagnosing and treating spinal conditions. Traditional deep learning methods depend on extensive, annotated datasets, which are hard to acquire. This research proposes an intensity-based self-supervised domain adaptation, using unlabeled multi-domain data to reduce reliance on large annotated datasets.

Methods

The study introduces an innovative method using intensity-based self-supervised learning for IVD segmentation in MRI scans. This approach is particularly suited for IVD segmentations due to its ability to effectively capture the subtle intensity variations that are characteristic of spinal structures. The model, a dual-task system, simultaneously segments IVDs and predicts intensity transformations. This

SUBMITTER: Fiorentino MC 

PROVIDER: S-EPMC11365836 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature