ArticlePhysics in medicine and biology2018
Geometric and dosimetric evaluations of atlas-based segmentation methods of MR images in the head and neck region.
Article in Physics in medicine and biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning algorithm performance in contouring head and neck organs at risk: a systematic review and single-arm meta-analysis.Biomedical engineering online · 2023Pooled it
- Trustworthy artificial intelligence in radiation oncology: cross-industry lessons for development, validation, and deployment.Frontiers in oncology · 2026Review
- Guidance on selecting and evaluating AI auto-segmentation systems in clinical radiotherapy: insights from a six-vendor analysis.Physical and engineering sciences in medicine · 2025Article
- Impact of annotation imperfections and auto-curation for deep learning-based organ-at-risk segmentation.Physics and imaging in radiation oncology · 2024Article
- Large-scale dose evaluation of deep learning organ contours in head-and-neck radiotherapy by leveraging existing plans.Physics and imaging in radiation oncology · 2024Article
- The Use of MR-Guided Radiation Therapy for Head and Neck Cancer and Recommended Reporting Guidance.Seminars in radiation oncology · 2024Review
- Geometric and dosimetric evaluation of deep learning based auto-segmentation for clinical target volume on breast cancer.Journal of applied clinical medical physics · 2023Article
- Extensive upfront validation and testing are needed prior to the clinical implementation of AI-based auto-segmentation tools.Journal of applied clinical medical physics · 2023Article
- The future of MRI in radiation therapy: Challenges and opportunities for the MR community.Magnetic resonance in medicine · 2022Review
- Dosimetric comparison of automatically propagated prostate contours with manually drawn contours in MRI-guided radiotherapy: A step towards a contouring free workflow?Clinical and translational radiation oncology · 2022Article
- An attention base U-net for parotid tumor autosegmentation.Frontiers in oncology · 2022Article
- Cross-modality deep learning: Contouring of MRI data from annotated CT data only.Medical physics · 2021Article
- Deep learning vs. atlas-based models for fast auto-segmentation of the masticatory muscles on head and neck CT images.Radiation oncology (London, England) · 2020Article
- External validation of deep learning-based contouring of head and neck organs at risk.Physics and imaging in radiation oncology · 2020Article
- A convolutional neural network for contouring metastatic lymph nodes on diffusion-weighted magnetic resonance images for assessment of radiotherapy response.Physics and imaging in radiation oncology · 2020Article
- Accuracy of automatic deformable structure propagation for high-field MRI guided prostate radiotherapy.Radiation oncology (London, England) · 2020Article
- Shape constrained fully convolutional DenseNet with adversarial training for multiorgan segmentation on head and neck CT and low-field MR images.Medical physics · 2019Article
- Synthetic 4D-CT of the thorax for treatment plan adaptation on MR-guided radiotherapy systems.Physics in medicine and biology · 2019Article
- A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning.Medical image analysis · 2019Article
- High-dimensional detection of imaging response to treatment in multiple sclerosis.NPJ digital medicine · 2019Article
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8 authors.
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Abstract
Owing to its excellent soft-tissue contrast, magnetic resonance (MR) imaging has found an increased application in radiation therapy (RT). By harnessing these properties for treatment planning, automated segmentation methods can alleviate the manual workload burden to the clinical workflow. We investigated atlas-based segmentation methods of organs at risk (OARs) in the head and neck (H&N) region using one approach that selected the most similar atlas from a library of segmented images and two multi-atlas approaches. The latter were based on weighted majority voting and an iterative atlas-fusion approach called STEPS. We built the atlas library from pre-treatment T1-weighted MR images of 12 patients with manual contours of the parotids, spinal cord and mandible, delineated by a clinician. Following a leave-one-out cross-validation strategy, we measured the geometric accuracy by calculating Dice similarity coefficients (DSC), standard and 95% Hausdorff distances (HD and HD95), and the mean surface distance (MSD), whereby the manual contours served as the gold standard. To benchmark the algorithm, we determined the inter-observer variability (IOV) between three observers. To investigate the dosimetric effect of segmentation inaccuracies, we implemented an auto-planning strategy within the treatment planning system Monaco (Elekta AB, Stockholm, Sweden). For each set of auto-segmented OARs, we generated a plan for a 9-beam step and shoot intensity modulated RT treatment, designed according to our institution's clinical H&N protocol. Superimposing the dose distributions on the gold standard OARs, we calculated dose differences to OARs caused by delineation differences between auto-segmented and gold standard OARs. We investigated the correlations between geometric and dosimetric differences. The mean DSC was larger than 0.8 and the mean MSD smaller than 2 mm for the multi-atlas approaches, resulting in a geometric accuracy comparable to previously published results and within the range of the IOV. While dosimetric differences could be as large as 23% of the clinical goal, treatment plans fulfilled all imposed clinical goals for the gold standard OARs. Correlations between geometric and dosimetric measures were low with R
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