ArticleScientific reports2024
Fully automated deep learning based auto-contouring of liver segments and spleen on contrast-enhanced CT images.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Single-photon emission computed tomography functional liver imaging to facilitate reirradiation for liver malignancies: A phase I trial.JHEP reports : innovation in hepatology · 2026Article
- [Minimally invasive liver resection-New techniques].Chirurgie (Heidelberg, Germany) · 2026Review
- Progress in fully automated abdominal CT interpretation-an update over the past decade.Abdominal radiology (New York) · 2026Review
- Association of quantitative renal surface nodularity with the renal dysfunction progression in patients with arterial hypertension.BMC medical imaging · 2025Article
- Precision in Liver Surgery: A Comparative Analysis of Volumetry Techniques.Annals of surgical oncology · 2025Article
- Advanced AI-driven detection of interproximal caries in bitewing radiographs using YOLOv8.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
Manual delineation of liver segments on computed tomography (CT) images for primary/secondary liver cancer (LC) patients is time-intensive and prone to inter/intra-observer variability. Therefore, we developed a deep-learning-based model to auto-contour liver segments and spleen on contrast-enhanced CT (CECT) images. We trained two models using 3d patch-based attention U-Net ([Formula: see text] and 3d full resolution of nnU-Net ([Formula: see text] to determine the best architecture ([Formula: see text]. BA was used with vessels ([Formula: see text] and spleen ([Formula: see text] to assess the impact on segment contouring. Models were trained, validated, and tested on 160 ([Formula: see text]), 40 ([Formula: see text]), 33 ([Formula: see text]), 25 (C
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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.