Fully automatic left ventricle segmentation in [Formula: see text]Rb PET/CT Using a semi-supervised nnU-net.

Area of Science:

• Cardiovascular Imaging
• Artificial Intelligence in Medicine
• Nuclear Cardiology

Background:

• Accurate quantification of myocardial blood flow (MBF) using Rubidium-82 (Rb-82) PET/CT necessitates precise left ventricle (LV) delineation.
• Manual or semi-automated LV segmentation is laborious and prone to errors, especially in areas of hypoperfused myocardium.

Purpose of the Study:

• To develop and validate a fully automatic LV segmentation pipeline using nnU-Net for Rb-82 PET/CT.
• To compare the performance of the automated pipeline against a semi-automatic thresholding baseline.

Main Methods:

• A manual, multimodal segmentation protocol was established as ground truth using dynamic PET and CT data from 40 patients.
• The nnU-Net model was trained using cross-validation and incorporated 805 additional unlabeled dynamic PET series via semi-supervised learning.
• Performance was evaluated against an optimized semi-automatic thresholding method.

Main Results:

• The nnU-Net pipeline significantly outperformed the baseline, achieving higher Dice scores (87.8% vs 75.1%), recall (89.1% vs 82.6%), and precision (88.1% vs 70.2%).
• Improvements were most significant in hypoperfused regions, with recall increasing by 20-30% compared to thresholding.
• Semi-supervised learning contributed to enhanced model robustness across rest and stress acquisitions.

Conclusions:

• A deep learning approach achieves fully automatic LV segmentation in Rb-82 PET/CT with accuracy comparable to expert performance.
• This automated framework eliminates manual segmentation, facilitating large-scale MBF quantification.
• The method supports reproducible, high-throughput cardiac PET analysis in clinical and research settings.