ArticleRadiation oncology (London, England)2021
Evaluation of auto-segmentation accuracy of cloud-based artificial intelligence and atlas-based models.
Article in Radiation oncology (London, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
32 citing papers in PubMed.
- Evaluation of segmentation accuracy and the improvement of time effectiveness using deep learning-based segmentation inEJNMMI physics · 2026Article
- Convolutional Neural Networks in Radiology: Principles, Clinical Applications, and a Practical Framework for Radiologists.Medical principles and practice : international journal of the Kuwait University, Health Science Centre · 2026Review
- Inter-observer variability in radiotherapy contouring with the use of autocontouring software: A systematic review.Clinical and translational radiation oncology · 2026Review
- Comparison between artificial intelligence-based and manual organ delineations in pretreatment computed tomography scans of prostate cancer patients: a visual grading study.Radiation protection dosimetry · 2026Article
- Enhancing Radiation Oncology Education Through Artificial Intelligence: A Review of Applications, Limitations, and Future Directions.Journal of cancer education : the official journal of the American Association for Cancer Education · 2025Review
- AI in Radiation Oncology: A Comprehensive Review of Current Applications and Future Directions.Cureus · 2025Review
- Deep learning-based delineation of whole-body organs at risk empowering adaptive radiotherapy.BMC medical informatics and decision making · 2025Article
- Physician-guided deep learning model for assessing thymic epithelial tumor volume.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Trade-off of different deep learning-based auto-segmentation approaches for treatment planning of pediatric craniospinal irradiation autocontouring of OARs for pediatric CSI.Medical physics · 2025Article
- Multidisciplinary Evaluation of an AI-Based Pneumothorax Detection Model: Clinical Comparison with Physicians in Edge and Cloud Environments.Journal of multidisciplinary healthcare · 2025Article
- Comparison of Vendor-Pretrained and Custom-Trained Deep Learning Segmentation Models for Head-and-Neck, Breast, and Prostate Cancers.Diagnostics (Basel, Switzerland) · 2024Article
- Impact of Artificial Intelligence-Based Autosegmentation of Organs at Risk in Low- and Middle-Income Countries.Advances in radiation oncology · 2024Article
- Geometric and dosimetric evaluation for breast and regional nodal auto-segmentation structures.Journal of applied clinical medical physics · 2024Article
- Investigation on performance of multiple AI-based auto-contouring systems in organs at risks (OARs) delineation.Physical and engineering sciences in medicine · 2024Article
- Gross failure rates and failure modes for a commercial AI-based auto-segmentation algorithm in head and neck cancer patients.Journal of applied clinical medical physics · 2024Article
- Evaluation of multiple-vendor AI autocontouring solutions.Radiation oncology (London, England) · 2024Article
- Clinical evaluation of the efficacy of limbus artificial intelligence software to augment contouring for prostate and nodes radiotherapy.The British journal of radiology · 2024Article
- Performance analysis and knowledge-based quality assurance of critical organ auto-segmentation for pediatric craniospinal irradiation.Scientific reports · 2024Article
- Revolutionizing radiation therapy: the role of AI in clinical practice.Journal of radiation research · 2024Review
- Application progress of artificial intelligence in tumor diagnosis and treatment.Frontiers in artificial intelligence · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
Abstract
backgroundContour delineation, a crucial process in radiation oncology, is time-consuming and inaccurate due to inter-observer variation has been a critical issue in this process. An atlas-based automatic segmentation was developed to improve the delineation efficiency and reduce inter-observer variation. Additionally, automated segmentation using artificial intelligence (AI) has recently become available. In this study, auto-segmentations by atlas- and AI-based models for Organs at Risk (OAR) in patients with prostate and head and neck cancer were performed and delineation accuracies were evaluated.
methodsTwenty-one patients with prostate cancer and 30 patients with head and neck cancer were evaluated. MIM Maestro was used to apply the atlas-based segmentation. MIM Contour ProtégéAI was used to apply the AI-based segmentation. Three similarity indices, the Dice similarity coefficient (DSC), Hausdorff distance (HD), and mean distance to agreement (MDA), were evaluated and compared with manual delineations. In addition, radiation oncologists visually evaluated the delineation accuracies.
resultsAmong patients with prostate cancer, the AI-based model demonstrated higher accuracy than the atlas-based on DSC, HD, and MDA for the bladder and rectum. Upon visual evaluation, some errors were observed in the atlas-based delineations when the boundary between the small bowel or the seminal vesicle and the bladder was unclear. For patients with head and neck cancer, no significant differences were observed between the two models for almost all OARs, except small delineations such as the optic chiasm and optic nerve. The DSC tended to be lower when the HD and the MDA were smaller in small volume delineations.
conclusionsIn terms of efficiency, the processing time for head and neck cancers was much shorter than manual delineation. While quantitative evaluation with AI-based segmentation was significantly more accurate than atlas-based for prostate cancer, there was no significant difference for head and neck cancer. According to the results of visual evaluation, less necessity of manual correction in AI-based segmentation indicates that the segmentation efficiency of AI-based model is higher than that of atlas-based model. The effectiveness of the AI-based model can be expected to improve the segmentation efficiency and to significantly shorten the delineation time.
Indexed as
Identifiers
What OpenQuestion holds
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.