Trial reportJournal of applied clinical medical physics2016
The feasibility of atlas-based automatic segmentation of MRI for H&N radiotherapy planning.
Trial report in Journal of applied clinical medical physics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Geometrically focused training and evaluation of organs-at-risk segmentation via deep learning.Medical physics · 2025Article
- A method for sensitivity analysis of automatic contouring algorithms across different contrast weightings using synthetic magnetic resonance imaging.Physics and imaging in radiation oncology · 2025Article
- The Use of MR-Guided Radiation Therapy for Head and Neck Cancer and Recommended Reporting Guidance.Seminars in radiation oncology · 2024Review
- Investigation of autosegmentation techniques on T2-weighted MRI for off-line dose reconstruction in MR-linac workflow for head and neck cancers.Medical physics · 2024Article
- Multi-organ auto-delineation in head-and-neck MRI for radiation therapy using regional convolutional neural network.Physics in medicine and biology · 2022Article
- Clinical outcomes of stereotactic magnetic resonance image-guided adaptive radiotherapy for primary and metastatic tumors in the abdomen and pelvis.Cancer medicine · 2021Article
- Geometric contour variation in clinical target volume of axillary lymph nodes in breast cancer radiotherapy: an AIRO multi-institutional study.The British journal of radiology · 2021Article
- Novel Autosegmentation Spatial Similarity Metrics Capture the Time Required to Correct Segmentations Better Than Traditional Metrics in a Thoracic Cavity Segmentation Workflow.Journal of digital imaging · 2021Article
- Automatic segmentation and applicator reconstruction for CT-based brachytherapy of cervical cancer using 3D convolutional neural networks.Journal of applied clinical medical physics · 2020Article
- Article
- Head and Neck Cancer Adaptive Radiation Therapy (ART): Conceptual Considerations for the Informed Clinician.Seminars in radiation oncology · 2019Review
- A study of positioning orientation effect on segmentation accuracy using convolutional neural networks for rectal cancer.Journal of applied clinical medical physics · 2019Article
- Magnetic Resonance Image-Guided Radiotherapy (MRIgRT): A 4.5-Year Clinical Experience.Clinical oncology (Royal College of Radiologists (Great Britain)) · 2018Article
- Segmentation of parotid glands from registered CT and MR images.Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) · 2018Article
- Geometric and dosimetric evaluations of atlas-based segmentation methods of MR images in the head and neck region.Physics in medicine and biology · 2018Article
- The effects of the orthopedic metal artifact reduction (O-MAR) algorithm on contouring and dosimetry of head and neck radiotherapy patients.Medical dosimetry : official journal of the American Association of Medical DosimetristsArticle
- Rhinoplasty Pre-Surgery Models by Using Low-Dose Computed Tomography, Magnetic Resonance Imaging, and 3D Printing.Dose-response : a publication of International Hormesis SocietyArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Atlas-based autosegmentation is an established tool for segmenting structures for CT-planned head and neck radiotherapy. MRI is being increasingly integrated into the planning process. The aim of this study is to assess the feasibility of MRI-based, atlas-based autosegmentation for organs at risk (OAR) and lymph node levels, and to compare the segmentation accuracy with CT-based autosegmentation. Fourteen patients with locally advanced head and neck cancer in a prospective imaging study underwent a T1-weighted MRI and a PET-CT (with dedicated contrast-enhanced CT) in an immobilization mask. Organs at risk (orbits, parotids, brainstem, and spinal cord) and the left level II lymph node region were manually delineated on the CT and MRI separately. A 'leave one out' approach was used to automatically segment structures onto the remaining images separately for CT and MRI. Contour comparison was performed using multiple positional metrics: Dice index, mean distance to conformity (MDC), sensitivity index (Se Idx), and inclusion index (Incl Idx). Automatic segmentation using MRI of orbits, parotids, brainstem, and lymph node level was acceptable with a DICE coefficient of 0.73-0.91, MDC 2.0-5.1mm, Se Idx 0.64-0.93, Incl Idx 0.76-0.93. Segmentation of the spinal cord was poor (Dice coefficient 0.37). The process of automatic segmentation was significantly better on MRI compared to CT for orbits, parotid glands, brainstem, and left lymph node level II by multiple positional metrics; spinal cord segmentation based on MRI was inferior compared with CT. Accurate atlas-based automatic segmentation of OAR and lymph node levels is feasible using T1-MRI; segmentation of the spinal cord was found to be poor. Comparison with CT-based automatic segmentation suggests that the process is equally as, or more accurate, using MRI. These results support further translation of MRI-based segmentation methodology into clinicalpractice.
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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.