ArticleJournal of medical Internet research2021
Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study.
Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 249 papers, 3 of them syntheses that pooled it.
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Who cites it
249 citing papers in PubMed, 3 syntheses or guidelines pooled it, 320 citations in OpenAlex.
- Vasculature segmentation in 3D hierarchical phase-contrast tomography images of human kidneys.Nature communications · 2026Pooled it
- Pooled it
- Comparisons of artificial intelligence automated segmentation techniques to manual segmentation techniques of the maxilla and maxillary sinus for CT or cone-beam CT scans-a systematic review.Dento maxillo facial radiology · 2025Pooled it
- Expert-centered Evaluation of Deep Learning Algorithms for Brain Tumor Segmentation.Radiology. Artificial intelligence · 2024Trial
- External evaluation of a deep learning-based algorithm for automated lung tumor and esophagus segmentation on CBCT.Biomedical physics & engineering express · 2026Article
- Optimizing the delivery of radiotherapy with artificial intelligence.Nature reviews. Clinical oncology · 2026Review
- A deep learning framework for automated quantification of peripheral nerve lesions in MR neurography.European radiology experimental · 2026Article
- Fusion of photon-counting CT and MRI improves assessment of the knee: a pilot study.Osteoarthritis imaging · 2026Article
- Real-world clinical impact of implementing and updating a deep learning-based automatic contouring system in rectal cancer radiotherapy.Journal of applied clinical medical physics · 2026Article
- Protect the Brain When Treating the Heart: Feasibility of 2.5D U-Net for Real-Time Gaseous Microemboli Detection.Bioengineering (Basel, Switzerland) · 2026Article
- Head-to-head comparison of pulsed-field ablation, high-power short-duration ablation, cryoballoon and conventional radiofrequency ablation by MRI-based ablation lesion assessment.Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing · 2026Article
- AI-Driven Three-Dimensional Reconstruction of Fetal Cardiovascular Pathways from Ultrasound Sweep Videos for Congenital Heart Disease Screening.Bioengineering (Basel, Switzerland) · 2026Article
- Artificial intelligence in oncology: linking biological discovery to clinical utility.Molecular cancer · 2026Review
- Freehand 3D ultrasound imaging of the femoral head and neck.International journal of computer assisted radiology and surgery · 2026Article
- Validation of MRI-based nnU-Net model for automated segmentation of neck lymph nodes in head and neck squamous cell carcinoma: a multicenter study.Neuroradiology · 2026Article
- The Case for Interpretable Geometry: Statistical Shape Models vs. Curvature-Based Descriptors in Aortic Disease Classification.medRxiv : the preprint server for health sciences · 2026Article
- Organ Segmentation with Machine Learning Models.Journal of imaging · 2026Article
- EGT-UNet: Evolutionary Game-Theoretic Adaptive Optimization for Pediatric Panoramic Tooth Segmentation.Bioengineering (Basel, Switzerland) · 2026Article
- Language-Guided Segmentation of Medical Images: A Review of Foundation Models.Bioengineering (Basel, Switzerland) · 2026Review
- Inter-observer variability in breast segmentation and its impact on focused ultrasound thermal therapy modeling.Physics in medicine and biology · 2026Article
189 more citing papers are in PubMed but not listed here.
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Authors and funding
29 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOver half a million individuals are diagnosed with head and neck cancer each year globally. Radiotherapy is an important curative treatment for this disease, but it requires manual time to delineate radiosensitive organs at risk. This planning process can delay treatment while also introducing interoperator variability, resulting in downstream radiation dose differences. Although auto-segmentation algorithms offer a potentially time-saving solution, the challenges in defining, quantifying, and achieving expert performance remain.
objectiveAdopting a deep learning approach, we aim to demonstrate a 3D U-Net architecture that achieves expert-level performance in delineating 21 distinct head and neck organs at risk commonly segmented in clinical practice.
methodsThe model was trained on a data set of 663 deidentified computed tomography scans acquired in routine clinical practice and with both segmentations taken from clinical practice and segmentations created by experienced radiographers as part of this research, all in accordance with consensus organ at risk definitions.
resultsWe demonstrated the model's clinical applicability by assessing its performance on a test set of 21 computed tomography scans from clinical practice, each with 21 organs at risk segmented by 2 independent experts. We also introduced surface Dice similarity coefficient, a new metric for the comparison of organ delineation, to quantify the deviation between organ at risk surface contours rather than volumes, better reflecting the clinical task of correcting errors in automated organ segmentations. The model's generalizability was then demonstrated on 2 distinct open-source data sets, reflecting different centers and countries to model training.
conclusionsDeep learning is an effective and clinically applicable technique for the segmentation of the head and neck anatomy for radiotherapy. With appropriate validation studies and regulatory approvals, this system could improve the efficiency, consistency, and safety of radiotherapy pathways.
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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.