ReviewQuantitative imaging in medicine and surgery2021
Deep learning for automatic target volume segmentation in radiation therapy: a review.
Review in Quantitative imaging in medicine and surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 43 citations in OpenAlex.
- Performance of Machine Learning for Tissue Outcome Prediction in Acute Ischemic Stroke: A Systematic Review and Meta-Analysis.Frontiers in neurology · 2022Pooled it
- Cross-fraction prior learning for scalable organ-at-risk segmentation in abdominal MR-guided radiotherapy.Medical physics · 2026Article
- A human-AI collaborative workflow for improved gross tumor volume delineation in esophageal cancer radiotherapy.BMC medical imaging · 2026Article
- Quantitative assessment of inter-observer consistency in target volume delineation following breast-conserving surgery and mastectomy.Frontiers in oncology · 2026Article
- A bias field correction workflow based on generative adversarial network for abdominal cancers treated with 0.35T MR-LINAC.Journal of applied clinical medical physics · 2026Article
- Deep learning-based applicator selection between Syed and T&O in high-dose-rate brachytherapy for locally advanced cervical cancer: a retrospective study.Physics in medicine and biology · 2025Article
- Performance Evaluation of Image Segmentation Using Dual-Energy Spectral CT Images with Deep Learning Image Reconstruction: A Phantom Study.Tomography (Ann Arbor, Mich.) · 2025Article
- Automatic segmentation for magnetic resonance imaging guided individual elective lymph node irradiation in head and neck cancer patients.Physics and imaging in radiation oncology · 2024Article
- The Integration of Deep Learning in Radiotherapy: Exploring Challenges, Opportunities, and Future Directions through an Umbrella Review.Diagnostics (Basel, Switzerland) · 2024Review
- Article
- Geometric and dosimetric evaluation of deep learning based auto-segmentation for clinical target volume on breast cancer.Journal of applied clinical medical physics · 2023Article
- Deep learning for segmentation of the cervical cancer gross tumor volume on magnetic resonance imaging for brachytherapy.Radiation oncology (London, England) · 2023Article
- Automatic deep learning method for detection and classification of breast lesions in dynamic contrast-enhanced magnetic resonance imaging.Quantitative imaging in medicine and surgery · 2023Article
- Excitement and Concerns of Young Radiation Oncologists over Automatic Segmentation: A French Perspective.Cancers · 2023Article
- Deep learning-based approach for the automatic segmentation of adult and pediatric temporal bone computed tomography images.Quantitative imaging in medicine and surgery · 2023Article
- Deep learning-based image analysis of eyelid morphology in thyroid-associated ophthalmopathy.Quantitative imaging in medicine and surgery · 2023Article
- Deep learning-based fully automated differential diagnosis of eyelid basal cell and sebaceous carcinoma using whole slide images.Quantitative imaging in medicine and surgery · 2022Article
- Automatic evaluation of endometrial receptivity in three-dimensional transvaginal ultrasound images based on 3D U-Net segmentation.Quantitative imaging in medicine and surgery · 2022Article
- 3D-Image-Guided Multi-Catheter Interstitial Brachytherapy for Bulky and High-Risk Stage IIB-IVB Cervical Cancer.Cancers · 2022Article
- Evaluation Exploration of Atlas-Based and Deep Learning-Based Automatic Contouring for Nasopharyngeal Carcinoma.Frontiers in oncology · 2022Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning, a new branch of machine learning algorithm, has emerged as a fast growing trend in medical imaging and become the state-of-the-art method in various clinical applications such as Radiology, Histo-pathology and Radiation Oncology. Specifically in radiation oncology, deep learning has shown its power in performing automatic segmentation tasks in radiation therapy for Organs-At-Risks (OAR), given its potential in improving the efficiency of OAR contouring and reducing the inter- and intra-observer variabilities. The similar interests were shared for target volume segmentation, an essential step of radiation therapy treatment planning, where the gross tumor volume is defined and microscopic spread is encompassed. The deep learning-based automatic segmentation method has recently been expanded into target volume automatic segmentation. In this paper, the authors summarized the major deep learning architectures of supervised learning fashion related to target volume segmentation, reviewed the mechanism of each infrastructure, surveyed the use of these models in various imaging domains (including Computational Tomography with and without contrast, Magnetic Resonant Imaging and Positron Emission Tomography) and multiple clinical sites, and compared the performance of different models using standard geometric evaluation metrics. The paper concluded with a discussion of open challenges and potential paths of future research in target volume automatic segmentation and how it may benefit the clinical practice.
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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.