ArticleJournal of digital imaging2023
Evaluation of Semiautomatic and Deep Learning-Based Fully Automatic Segmentation Methods on [
Article in Journal of digital imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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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.
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
16 citing papers in PubMed.
- Automatic metabolic breast cancer staging using [¹⁸F]FDG PET/CT: comparison with nuclear medicine physician-based and clinical staging.European journal of nuclear medicine and molecular imaging · 2026Article
- A convolutional neural network for fully automated total metabolic tumor volume delineation in patients with aggressive Non-Hodgkin lymphoma.European journal of nuclear medicine and molecular imaging · 2026Article
- Article
- An AI-Based Solution for Denoising Fast-Acquisition [Journal of imaging informatics in medicine · 2026Article
- A Lesion-adaptive Segmentation Approach for Tumor Delineation on FDG PET/CT in Diffuse Large B-cell Lymphoma Patients.European journal of nuclear medicine and molecular imaging · 2026Article
- Segmentation method comparison for baseline [EJNMMI physics · 2026Article
- Lymph node disease in 2-deoxy-2-fluorodeoxyglucose positron emission tomography/computed tomography imaging: Advances in artificial intelligence-driven automatic segmentation and precise diagnosis.World journal of clinical oncology · 2025Review
- Automatic Quantification of Serial PET/CT Images for Pediatric Hodgkin Lymphoma Using a Longitudinally Aware Segmentation Network.Radiology. Artificial intelligence · 2025Article
- Development and validation of pan-cancer lesion segmentation AI-model for whole-body 18F-FDG PET/CT in diverse clinical cohorts.Computers in biology and medicine · 2025Article
- Artificial intelligence algorithm for preoperative prediction of FIGO stage in ovarian cancer based on clinical features integrated 18F-FDG PET/CT metabolic and radiomics features.Journal of cancer research and clinical oncology · 2025Article
- Review
- Baseline [European journal of nuclear medicine and molecular imaging · 2024Article
- Article
- The role of FDG PET assessment in patients with advanced breast cancer treated with cyclin-dependent kinase 4/6 inhibitors in the second-line setting.Frontiers in oncology · 2024Article
- Machine Learning CT-Based Automatic Nodal Segmentation and PET Semi-Quantification of IntraoperativeDiagnostics (Basel, Switzerland) · 2023Article
- Recent advances in deep learning for lymphoma segmentation: Clinical applications and challenges.Digital healthReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The objective is to assess the performance of seven semiautomatic and two fully automatic segmentation methods on [
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.