ArticleBMC medical imaging2014
3D facial landmarks: Inter-operator variability of manual annotation.
Article in BMC medical imaging, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advancement in Human Face Prediction Using DNA.Genes · 2023Pooled it
- Modernizing Forensic Anthropology: A Data-driven Pipeline for Human Identification and Profiling.European journal of dentistry · 2026Article
- A Fully Automated Deep Learning Pipeline for Anatomical Landmark Localization on Three-Dimensional Pelvic Surface Scans.Sensors (Basel, Switzerland) · 2026Article
- A personalized automated system of 3D facial soft tissue landmarks annotation based on deep learning and computer vision.BMC oral health · 2025Article
- Deep-Learning Model for Iris and Eyebrow Segmentation and Automation of Eye Landmark Measurements in Acquired Ptosis.Biomedicines · 2025Article
- Precision Assessment of Facial Asymmetry Using 3D Imaging and Artificial Intelligence.Journal of clinical medicine · 2025Article
- [Establishment and evaluation of a similarity measurement model for orthognathic patients based on the 3D craniofacial features].Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences · 2025Article
- Patch-based convolutional neural networks for automatic landmark detection of 3D facial images in clinical settings.European journal of orthodontics · 2024Article
- Deep Learning-Based Fine-Tuning Approach of Coarse Registration for Ear-Nose-Throat (ENT) Surgical Navigation Systems.Bioengineering (Basel, Switzerland) · 2024Article
- The Ability to Sustain Facial Expressions.The Journal of craniofacial surgery · 2024Article
- Article
- Automatic Neurocranial Landmarks Detection from Visible Facial Landmarks Leveraging 3D Head Priors.Clinical image-based procedures, fairness of AI in medical imaging, and ethical and philosophical issues in medical imaging : 12th International Workshop, CLIP 2023 1st International Workshop, FAIMI 2023 and 2nd International Workshop, ... · 2023Article
- Comparison Study of Extraction Accuracy of 3D Facial Anatomical Landmarks Based on Non-Rigid Registration of Face Template.Diagnostics (Basel, Switzerland) · 2023Article
- [Automatic determination of mandibular landmarks based on three-dimensional mandibular average model].Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences · 2023Article
- Article
- Creating Three-Dimensional Templates of Smiling and Pouting Faces for Different Sex- and Age Groups.Journal of clinical medicine · 2022Article
- Testing inter-observer error under a collaborative research framework for studying lithic shape variability.Archaeological and anthropological sciences · 2022Article
- Automatic 3D dense phenotyping provides reliable and accurate shape quantification of the human mandible.Scientific reports · 2021Article
- Integration of digital maxillary dental casts with 3D facial images in orthodontic patients.The Angle orthodontist · 2020Article
- Sources of variation in the 3dMDface and Vectra H1 3D facial imaging systems.Scientific reports · 2020Article
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Authors and funding
7 authors.
Funding
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Abstract
backgroundManual annotation of landmarks is a known source of variance, which exist in all fields of medical imaging, influencing the accuracy and interpretation of the results. However, the variability of human facial landmarks is only sparsely addressed in the current literature as opposed to e.g. the research fields of orthodontics and cephalometrics. We present a full facial 3D annotation procedure and a sparse set of manually annotated landmarks, in effort to reduce operator time and minimize the variance.
methodFacial scans from 36 voluntary unrelated blood donors from the Danish Blood Donor Study was randomly chosen. Six operators twice manually annotated 73 anatomical and pseudo-landmarks, using a three-step scheme producing a dense point correspondence map. We analyzed both the intra- and inter-operator variability, using mixed-model ANOVA. We then compared four sparse sets of landmarks in order to construct a dense correspondence map of the 3D scans with a minimum point variance.
resultsThe anatomical landmarks of the eye were associated with the lowest variance, particularly the center of the pupils. Whereas points of the jaw and eyebrows have the highest variation. We see marginal variability in regards to intra-operator and portraits. Using a sparse set of landmarks (n=14), that capture the whole face, the dense point mean variance was reduced from 1.92 to 0.54 mm.
conclusionThe inter-operator variability was primarily associated with particular landmarks, where more leniently landmarks had the highest variability. The variables embedded in the portray and the reliability of a trained operator did only have marginal influence on the variability. Further, using 14 of the annotated landmarks we were able to reduced the variability and create a dense correspondences mesh to capture all facial features.
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