ArticleScientific reports2019
Dual-energy CT for automatic organs-at-risk segmentation in brain-tumor patients using a multi-atlas and deep-learning approach.
Article in Scientific reports, 2019. 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.
- Spectral CT and Photon-counting Detector CT: Principles and Applications in Neuroradiology.Radiology. Imaging cancer · 2026Review
- Generalizability, robustness, and correction bias of segmentations of thoracic organs at risk in CT images.European radiology · 2025Article
- The evolution and current situation in the application of dual-energy computed tomography: a bibliometric study.Quantitative imaging in medicine and surgery · 2023Article
- Inpainting truncated areas of CT images based on generative adversarial networks with gated convolution for radiotherapy.Medical & biological engineering & computing · 2023Article
- Synthetic cranial MRI from 3D optical surface scans using deep learning for radiation therapy treatment planning.Physical and engineering sciences in medicine · 2023Article
- Impact of Denoising on Deep-Learning-Based Automatic Segmentation Framework for Breast Cancer Radiotherapy Planning.Cancers · 2022Article
- Artificial Intelligence in Radiation Therapy.IEEE transactions on radiation and plasma medical sciences · 2022Article
- Improving radiation physics, tumor visualisation, and treatment quantification in radiotherapy with spectral or dual-energy CT.Journal of applied clinical medical physics · 2022Review
- Evaluation of novel AI-based extended field-of-view CT reconstructions.Medical physics · 2021Article
- Head and neck multi-organ segmentation on dual-energy CT using dual pyramid convolutional neural networks.Physics in medicine and biology · 2021Article
- A review of deep learning based methods for medical image multi-organ segmentation.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) · 2021Review
- Quantitative salivary gland SPECT/CT using deep convolutional neural networks.Scientific reports · 2021Article
- A Segmentation Method of Foramen Ovale Based on Multiatlas.Computational and mathematical methods in medicine · 2021Article
- Deep learning approach for quantification of organelles and misfolded polypeptide delivery within degradative compartments.Molecular biology of the cell · 2020Article
- Status and innovations in pre-treatment CT imaging for proton therapy.The British journal of radiology · 2020Review
- Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges.Brain sciences · 2020Review
- Artificial Intelligence: reshaping the practice of radiological sciences in the 21st century.The British journal of radiology · 2020Review
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7 authors.
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Abstract
In radiotherapy, computed tomography (CT) datasets are mostly used for radiation treatment planning to achieve a high-conformal tumor coverage while optimally sparing healthy tissue surrounding the tumor, referred to as organs-at-risk (OARs). Based on CT scan and/or magnetic resonance images, OARs have to be manually delineated by clinicians, which is one of the most time-consuming tasks in the clinical workflow. Recent multi-atlas (MA) or deep-learning (DL) based methods aim to improve the clinical routine by an automatic segmentation of OARs on a CT dataset. However, so far no studies investigated the performance of these MA or DL methods on dual-energy CT (DECT) datasets, which have been shown to improve the image quality compared to conventional 120 kVp single-energy CT. In this study, the performance of an in-house developed MA and a DL method (two-step three-dimensional U-net) was quantitatively and qualitatively evaluated on various DECT-derived pseudo-monoenergetic CT datasets ranging from 40 keV to 170 keV. At lower energies, the MA method resulted in more accurate OAR segmentations. Both the qualitative and quantitative metric analysis showed that the DL approach often performed better than the MA method.
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