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

Mohammadreza Amirian^1,2, Arthur Chevalley^1,2, María Martín Asiain^1,2

¹Department of Nuclear Medicine and Molecular Imaging, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland.

EJNMMI Research

|May 28, 2026

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This summary is machine-generated.

A novel deep learning method automates left ventricle segmentation for Rubidium-82 PET/CT scans. This fully automatic pipeline improves accuracy and efficiency in myocardial blood flow quantification.

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.

Keywords:

[Formula: see text]Rb PET/CT Deep convolutional neural networks Left ventricle segmentation

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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.