ArticleMolecular imaging and biology2023
Manual Versus Artificial Intelligence-Based Segmentations as a Pre-processing Step in Whole-body PET Dosimetry Calculations.
Article in Molecular imaging and biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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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.
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Who cites it
8 citing papers in PubMed.
- Validation of an automated AI-based micro-CT organ segmentation workflow against expert annotations and its impact on fluorescence quantification.European radiology experimental · 2026Article
- Evaluation of segmentation accuracy and the improvement of time effectiveness using deep learning-based segmentation inEJNMMI physics · 2026Article
- First human whole-body biodistribution and dosimetry analysis of [European journal of nuclear medicine and molecular imaging · 2026Article
- Inter-institutional variability in kidney dosimetry duringRadiological physics and technology · 2026Article
- Artificial intelligence and machine learning-driven advancements in gastrointestinal cancer: Paving the way for precision medicine.World journal of gastroenterology · 2026Review
- Towards practical radiopharmaceutical treatment planning: a review of dosimetry simplification techniques.Theranostics · 2026Review
- Future of radiotheranostics.BMJ oncology · 2026Review
- Activity quantification and dosimetry in radiopharmaceutical therapy with reference toFrontiers in nuclear medicine · 2024Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
purposeAs novel tracers are continuously under development, it is important to obtain reliable radiation dose estimates to optimize the amount of activity that can be administered while keeping radiation burden within acceptable limits. Organ segmentation is required for quantification of specific uptake in organs of interest and whole-body dosimetry but is a time-consuming task which induces high interobserver variability. Therefore, we explored using manual segmentations versus an artificial intelligence (AI)-based automated segmentation tool as a pre-processing step for calculating whole-body effective doses to determine the influence of variability in volumetric whole-organ segmentations on dosimetry. PROCEDURES: PET/CT data of six patients undergoing imaging with
resultsData were acquired on days 2, 4, and 7 post-injection, resulting in 18 scans. Overall analysis time per scan was approximately 4 h for manual segmentations compared to ≤ 30 min using AI-based segmentations. Median Jaccard similarity coefficients between manual and AI-based segmentations varied from 0.05 (range 0.00-0.14) for the pancreas to 0.78 (range 0.74-0.82) for the lungs. Whole-body effective doses differed minimally for the six patients with a median difference in received mSv/MBq of 0.52% (range 0.15-1.95%).
conclusionThis pilot study suggests that whole-body dosimetry calculations can benefit from fast, automated AI-based whole organ segmentations.
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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.