ArticleScientific data2019
Computed tomography data collection of the complete human mandible and valid clinical ground truth models.
Article in Scientific data, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed.
- Using computed tomography, photogrammetry & structured light scanning for measuring human mandibles: a comparison of metric analysis using various 3D imaging technologies.International journal of legal medicine · 2026Article
- Geometric Fidelity of Magnetic Resonance Imaging and Computed Tomography-Derived Virtual 3D Models of Porcine Cadaver Mandibles: Conventional Versus Artificial Intelligence-Based Segmentation.Oral health & preventive dentistry · 2025Article
- Automated segmentation of the fibula from CT imaging using two-stepped deep learning in 3D U-Net architectures.Scientific reports · 2025Article
- Innovations in gender affirmation: AI-enhanced surgical guides for mandibular facial feminization surgery.Clinical oral investigations · 2025Article
- Features and networks of the mandible on computed tomography.Royal Society open science · 2024Article
- Large scale crowdsourced radiotherapy segmentations across a variety of cancer anatomic sites.Scientific data · 2023Article
- Graph-Based Motion Artifacts Detection Method from Head Computed Tomography Images.Sensors (Basel, Switzerland) · 2022Article
- Studierfenster: an Open Science Cloud-Based Medical Imaging Analysis Platform.Journal of digital imaging · 2022Article
- Automatic Segmentation of Mandible from Conventional Methods to Deep Learning-A Review.Journal of personalized medicine · 2021Review
- Recurrent Convolutional Neural Networks for 3D Mandible Segmentation in Computed Tomography.Journal of personalized medicine · 2021Article
- Robust and Accurate Mandible Segmentation on Dental CBCT Scans Affected by Metal Artifacts Using a Prior Shape Model.Journal of personalized medicine · 2021Article
- Deep learning-a first meta-survey of selected reviews across scientific disciplines, their commonalities, challenges and research impact.PeerJ. Computer science · 2021Article
- Introducing Biomedisa as an open-source online platform for biomedical image segmentation.Nature communications · 2020Article
- Facial model collection for medical augmented reality in oncologic cranio-maxillofacial surgery.Scientific data · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Image-based algorithmic software segmentation is an increasingly important topic in many medical fields. Algorithmic segmentation is used for medical three-dimensional visualization, diagnosis or treatment support, especially in complex medical cases. However, accessible medical databases are limited, and valid medical ground truth databases for the evaluation of algorithms are rare and usually comprise only a few images. Inaccuracy or invalidity of medical ground truth data and image-based artefacts also limit the creation of such databases, which is especially relevant for CT data sets of the maxillomandibular complex. This contribution provides a unique and accessible data set of the complete mandible, including 20 valid ground truth segmentation models originating from 10 CT scans from clinical practice without artefacts or faulty slices. From each CT scan, two 3D ground truth models were created by clinical experts through independent manual slice-by-slice segmentation, and the models were statistically compared to prove their validity. These data could be used to conduct serial image studies of the human mandible, evaluating segmentation algorithms and developing adequate image tools.
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Registered trials
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