ArticleEJNMMI research2026
Fully automatic left ventricle segmentation in [Formula: see text]Rb PET/CT Using a semi-supervised nnU-net.
Article in EJNMMI research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
backgroundQuantification of myocardial blood flow (MBF) with [Formula: see text]Rb PET/CT requires accurate delineation of the left ventricle (LV). Manual or semi-automated contouring remains time-consuming and error-prone, particularly in hypoperfused myocardium. We developed and validated a fully automatic LV segmentation pipeline using nnU-Net applied to [Formula: see text]Rb PET/CT. A manual, multimodal segmentation protocol integrating dynamic PET and CT was established in a single-center cohort of 40 non-gated PET/CT series (20 patients, rest & stress), including challenging cases with extensive necrosis (median 21%). The resulting ground truth masks were used for five-fold cross-validation, and semi-supervised learning incorporated 805 additional unlabeled dynamic PET series (504 patients). Model performance was compared with an optimized semi-automatic thresholding baseline (35% [Formula: see text]).
resultsThe nnU-Net significantly outperformed the baseline, achieving a mean Dice of 87.8[85.6, 89.2]% vs 75.1[72.9, 76.9]%, recall 89.1[86.1, 91.4]% vs 82.6[79.1, 85.4]%, and precision 88.1[84.2, 90.4]% vs 70.2[67.2, 73.0]%. The improvement was most pronounced in hypoperfused regions, where recall increased by 20-30% compared to thresholding. Semi-supervised learning modestly enhanced model robustness across both rest and stress acquisitions.
conclusionsA deep-learning-based approach enables fully automatic LV segmentation in [Formula: see text]Rb PET/CT with near-expert accuracy. This framework eliminates manual interaction, supports large-scale MBF quantification, and paves the way for reproducible, high-throughput cardiac PET analysis in clinical and research workflows.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.