Automatic Segmentation of Clinical Target Volume and Organs-at-Risk for Breast Conservative Radiotherapy Using a Convolutional Neural Network
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Summary
The proposed model (U-ResNet) can improve the efficiency and accuracy of delineation compared with U-Net, performing equally well with the segmentation generated by oncologists.
- Type
- article
- Published
- 2021-06-02
- Cited by
- 21
- References
- 37
- Access
- Open access
- OpenAlex
- https://openalex.org/W3168215917
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:236221113
Keywords
Medicine, Percentile, Radiation oncologist, Nuclear medicine, Radiation therapy
References
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- Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks
- Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks
Cited by
- Training, validation, and clinical implementation of a deep-learning segmentation model for radiotherapy of loco-regional breast cancer.
- Using Artificial Intelligence for Optimization of the Processes and Resource Utilization in Radiotherapy
- Excitement and Concerns of Young Radiation Oncologists over Automatic Segmentation: A French Perspective
- Extensive clinical testing of Deep Learning Segmentation models for thorax and breast cancer radiotherapy planning
- Review of Deep Learning Based Autosegmentation for Clinical Target Volume: Current Status and Future Directions
- A review of the development of intelligent delineation of radiotherapy contouring
- Clinical evaluation of the efficacy of limbus artificial intelligence software to augment contouring for prostate and nodes radiotherapy
- Comparison of the use of a clinically implemented deep learning segmentation model with the simulated study setting for breast cancer patients receiving radiotherapy
- Performance of Commercial Deep Learning-Based Auto-Segmentation Software for Breast Cancer Radiation Therapy Planning: A Systematic Review
- Development and external multicentric validation of a deep learning-based clinical target volume segmentation model for whole-breast radiotherapy
- Real world AI-driven segmentation: Efficiency gains and workflow challenges in radiotherapy.
- Artificial intelligence in breast cancer radiotherapy: Insights from the Toolbox Consortium Delphi study
- Artificial intelligence as treatment support in breast cancer: current perspectives
- Evaluation of compartmentalized automatic segmentation for definition of the GTV in glioblastoma radiotherapy.
- A dual-layer quality assurance approach leveraging dose prediction for efficient review of automated contours of organs at risk in the brain in radiotherapy
- MSBC-Segformer: An automatic segmentation model of clinical target volume and organs at risk in CT images for radiotherapy after breast-conserving surgery
- Automatic segmentation of clinical target volume for radiation therapy in breast-conserving patients and exploration of clinical factors influential to its performance
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