ArticleScientific reports2022
Multi-organ segmentation of abdominal structures from non-contrast and contrast enhanced CT images.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Anatomic Interactive Atlas of the Loggerhead Sea Turtle (Animals : an open access journal from MDPI · 2026Article
- Discerning clinical target volume of endometrial cancer via a lightweight multi-phase delineation framework.Radiation oncology (London, England) · 2026Article
- The Impact of Radiation Dose to Bowel on the Risk of Developing Gastrointestinal Toxicities When Treating Abdominopelvic Soft-Tissue Sarcomas With Preoperative Radiation Therapy.Advances in radiation oncology · 2026Article
- Open-source domain adaptation to handle data shift for volumetric segmentation-use case kidney segmentation.European radiology · 2026Article
- Impact of deep learning on CT-based organ-at-risk delineation for flank irradiation in paediatric renal tumours: a SIOP-RTSG radiotherapy committee study.Clinical and translational radiation oncology · 2026Article
- Automatic detection of urinary stones from non-contrast enhanced computed tomography images.Urolithiasis · 2025Article
- Global and regional accuracy of deep learning-based tumor segmentation from whole-body [EJNMMI research · 2025Article
- Clinical Applications of Quantitative Imaging and Artificial Intelligence for Pancreatic Cance.Seminars in radiation oncology · 2025Review
- Children Are Not Small Adults: Addressing Limited Generalizability of an Adult Deep Learning CT Organ Segmentation Model to the Pediatric Population.Journal of imaging informatics in medicine · 2025Article
- Assessing Quantitative Performance and Expert Review of Multiple Deep Learning-Based Frameworks for Computed Tomography-based Abdominal Organ Auto-Segmentation.Intelligent oncology · 2025Article
- Enhanced Domain Adaptation for Foot Ulcer Segmentation Through Mixing Self-Trained Weak Labels.Journal of imaging informatics in medicine · 2025Article
- Automated Segmentation of Lymph Nodes on Neck CT Scans Using Deep Learning.Journal of imaging informatics in medicine · 2024Article
- Landmark-based auto-contouring of clinical target volumes for radiotherapy of nasopharyngeal cancer.Journal of applied clinical medical physics · 2024Article
- Autocontouring of primary lung lesions and nodal disease for radiotherapy based only on computed tomography images.Physics and imaging in radiation oncology · 2024Article
- Improved automatic segmentation of brain metastasis gross tumor volume in computed tomography images for radiotherapy: a position attention module for U-Net architecture.Quantitative imaging in medicine and surgery · 2024Article
- Evolving Horizons in Radiation Therapy Auto-Contouring: Distilling Insights, Embracing Data-Centric Frameworks, and Moving Beyond Geometric Quantification.Advances in radiation oncology · 2024Article
- Automatic end-to-end VMAT treatment planning for rectal cancers.Journal of applied clinical medical physics · 2024Article
- Fully automated deep learning based auto-contouring of liver segments and spleen on contrast-enhanced CT images.Scientific reports · 2024Article
- Fully-automated, CT-only GTV contouring for palliative head and neck radiotherapy.Scientific reports · 2023Article
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Authors and funding
18 authors.
Funding
Abstract
Manually delineating upper abdominal organs at risk (OARs) is a time-consuming task. To develop a deep-learning-based tool for accurate and robust auto-segmentation of these OARs, forty pancreatic cancer patients with contrast-enhanced breath-hold computed tomographic (CT) images were selected. We trained a three-dimensional (3D) U-Net ensemble that automatically segments all organ contours concurrently with the self-configuring nnU-Net framework. Our tool's performance was assessed on a held-out test set of 30 patients quantitatively. Five radiation oncologists from three different institutions assessed the performance of the tool using a 5-point Likert scale on an additional 75 randomly selected test patients. The mean (± std. dev.) Dice similarity coefficient values between the automatic segmentation and the ground truth on contrast-enhanced CT images were 0.80 ± 0.08, 0.89 ± 0.05, 0.90 ± 0.06, 0.92 ± 0.03, 0.96 ± 0.01, 0.97 ± 0.01, 0.96 ± 0.01, and 0.96 ± 0.01 for the duodenum, small bowel, large bowel, stomach, liver, spleen, right kidney, and left kidney, respectively. 89.3% (contrast-enhanced) and 85.3% (non-contrast-enhanced) of duodenum contours were scored as a 3 or above, which required only minor edits. More than 90% of the other organs' contours were scored as a 3 or above. Our tool achieved a high level of clinical acceptability with a small training dataset and provides accurate contours for treatment planning.
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