ArticleMedical physics2022
Pediatric chest-abdomen-pelvis and abdomen-pelvis CT images with expert organ contours.
Article in Medical physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Anatomical study and meta-analysis of the episternal ossicles.Surgical and radiologic anatomy : SRA · 2024Pooled it
- 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
- Patient-specific organ dose calculation using automatic segmentation and extension of CT scans.Radiation physics and chemistry (Oxford, England : 1993) · 2025Article
- Subject-specific modeling framework for particle deposition using computational fluid dynamics.Journal of aerosol science · 2025Article
- Lack of children in public medical imaging data points to growing age bias in biomedical AI.Research square · 2025Article
- Technical Note: Neural Network Architectures for Self-Supervised Body Part Regression Models with Automated Localized Segmentation Application.Journal of imaging informatics in medicine · 2025Article
- A Geometric Morphometric Study of Scapular Ontogeny in Modern Humans.American journal of biological anthropology · 2025Article
- Lack of children in public medical imaging data points to growing age bias in biomedical AI.medRxiv : the preprint server for health sciences · 2025Article
- 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
- Deep learning-based segmentation of ultra-low-dose CT images using an optimized nnU-Net model.La Radiologia medica · 2025Article
- A robust auto-contouring and data augmentation pipeline for adaptive MRI-guided radiotherapy of pancreatic cancer with a limited dataset.Physics in medicine and biology · 2025Article
- Realistic extension of partial-body pediatric CT for whole-body organ dose estimation in radiotherapy patients.Radiation physics and chemistry (Oxford, England : 1993) · 2025Article
- Computational multiphysics modeling of radioactive aerosol deposition in diverse human respiratory tract geometries.Communications engineering · 2024Article
- Applications of Artificial Intelligence for Pediatric Cancer Imaging.AJR. American journal of roentgenology · 2024Review
- Age Encoded Adversarial Learning for Pediatric CT Segmentation.Bioengineering (Basel, Switzerland) · 2024Article
- National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence.Radiographics : a review publication of the Radiological Society of North America, Inc · 2023Article
- Technical note: Evaluation of a V-Net autosegmentation algorithm for pediatric CT scans: Performance, generalizability, and application to patient-specific CT dosimetry.Medical physics · 2022Article
- Reduced Chest Computed Tomography Scan Length for Patients Positive for Coronavirus Disease 2019: Dose Reduction and Impact on Diagnostic Utility.Journal of computer assisted tomographyArticle
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Authors and funding
16 authors.
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Abstract
purposeOrgan autosegmentation efforts to date have largely been focused on adult populations, due to limited availability of pediatric training data. Pediatric patients may present additional challenges for organ segmentation. This paper describes a dataset of 359 pediatric chest-abdomen-pelvis and abdomen-pelvis Computed Tomography (CT) images with expert contours of up to 29 anatomical organ structures to aid in the evaluation and development of autosegmentation algorithms for pediatric CT imaging. ACQUISITION AND VALIDATION
methodsThe dataset collection consists of axial CT images in Digital Imaging and Communications in Medicine (DICOM) format of 180 male and 179 female pediatric chest-abdomen-pelvis or abdomen-pelvis exams acquired from one of three CT scanners at Children's Wisconsin. The datasets represent random pediatric cases based upon routine clinical indications. Subjects ranged in age from 5 days to 16 years, with a mean age of 7 years. The CT acquisition, contrast, and reconstruction protocols varied across the scanner models and patients, with specifications available in the DICOM headers. Expert contours were manually labeled for up to 29 organ structures per subject. Not all contours are available for all subjects, due to limited field of view or unreliable contouring due to high noise. DATA FORMAT AND USAGE NOTES: The data are available on The Cancer Imaging Archive (TCIA_ (https://www.cancerimagingarchive.net/) under the collection Pediatric-CT-SEG. The axial CT image slices for each subject are available in DICOM format. The expert contours are stored in a single DICOM RTSTRUCT file for each subject. The contour names are listed in Table 2. POTENTIAL APPLICATIONS: This dataset will enable the evaluation and development of organ autosegmentation algorithms for pediatric populations, which exhibit variations in organ shape and size across age. Automated organ segmentation from CT images has numerous applications including radiation therapy, diagnostic tasks, surgical planning, and patient-specific organ dose estimation.
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