Annotation-efficient deep learning detection and measurement of mediastinal lymph nodes in CT.

ABSTRACT:

Purpose

Manual detection and measurement of structures in volumetric scans is routine in clinical practice but is time-consuming and subject to observer variability. Automatic deep learning-based solutions are effective but require a large dataset of manual annotations by experts. We present a novel annotation-efficient semi-supervised deep learning method for automatic detection, segmentation, and measurement of the short axis length (SAL) of mediastinal lymph nodes (LNs) in contrast-enhanced CT (ceCT) scans.

Methods

Our semi-supervised method combines the precision of expert annotations with the quantity advantages of pseudolabeled data. It uses an ensemble of 3D nnU-Net models trained on a few expert-annotated scans to generate pseudolabels on a large dataset of unannotat

SUBMITTER: Olesinski A 

PROVIDER: S-EPMC12929302 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature