Fast, light, and scalable: harnessing data-mined line annotations for automated tumor segmentation on brain MRI.

ABSTRACT:

Objectives

While fully supervised learning can yield high-performing segmentation models, the effort required to manually segment large training sets limits practical utility. We investigate whether data mined line annotations can facilitate brain MRI tumor segmentation model development without requiring manually segmented training data.

Methods

In this retrospective study, a tumor detection model trained using clinical line annotations mined from PACS was leveraged with unsupervised segmentation to generate pseudo-masks of enhancing tumors on T1-weighted post-contrast images (9911 image slices; 3449 adult patients). Baseline segmentation models were trained and employed within a semi-supervised learning (SSL) framework to refine the pseudo-masks. Following each self-refine

SUBMITTER: Swinburne NC 

PROVIDER: S-EPMC10523913 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature