AIMC Topic: Organs at Risk

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Evaluation of auto-segmentation accuracy of cloud-based artificial intelligence and atlas-based models.

Radiation oncology (London, England) Sep 9, 2021
BACKGROUND: Contour 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 d...
Humans Artificial Intelligence Male Observer Variation Cloud Computing Atlases as Topic Head and Neck Neoplasms Prostatic Neoplasms Organs at Risk
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Deep learning method for prediction of patient-specific dose distribution in breast cancer.

Radiation oncology (London, England) Aug 17, 2021
BACKGROUND: Patient-specific dose prediction improves the efficiency and quality of radiation treatment planning and reduces the time required to find the optimal plan. In this study, a patient-specific dose prediction model was developed for a left-...
Breast Neoplasms Middle Aged Deep Learning Humans Female Radiotherapy, Intensity-Modulated Organs at Risk Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage
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Clinical implementation of deep-learning based auto-contouring tools-Experience of three French radiotherapy centers.

Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique Aug 11, 2021
Deep-learning (DL)-based auto-contouring solutions have recently been proposed as a convincing alternative to decrease workload of target volumes and organs-at-risk (OAR) delineation in radiotherapy planning and improve inter-observer consistency. Ho...
Practice Guidelines as Topic Radiotherapy, Image-Guided Societies, Medical Deep Learning Europe Radiation Oncology Workload Organs at Risk Radiotherapy Planning, Computer-Assisted Humans Neoplasms
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Deep learning for segmentation in radiation therapy planning: a review.

Journal of medical imaging and radiation oncology Jul 26, 2021
Segmentation of organs and structures, as either targets or organs-at-risk, has a significant influence on the success of radiation therapy. Manual segmentation is a tedious and time-consuming task for clinicians, and inter-observer variability can a...
Organs at Risk Radiotherapy Planning, Computer-Assisted Tomography, X-Ray Computed Humans Deep Learning Image Processing, Computer-Assisted
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Development of in-house fully residual deep convolutional neural network-based segmentation software for the male pelvic CT.

Radiation oncology (London, England) Jul 22, 2021
BACKGROUND: This study aimed to (1) develop a fully residual deep convolutional neural network (CNN)-based segmentation software for computed tomography image segmentation of the male pelvic region and (2) demonstrate its efficiency in the male pelvi...
Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Radiotherapy, Intensity-Modulated Urinary Bladder Seminal Vesicles Prostatic Neoplasms Pelvis Organs at Risk Male Image Processing, Computer-Assisted Neural Networks, Computer Humans Software Prognosis
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A feasibility study on deep learning-based individualized 3D dose distribution prediction.

Medical physics Jul 11, 2021
PURPOSE: Radiation therapy treatment planning is a trial-and-error, often time-consuming process. An approximately optimal dose distribution corresponding to a specific patient's anatomy can be predicted by using pre-trained deep learning (DL) models...
Organs at Risk Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Male Deep Learning Feasibility Studies Radiotherapy, Intensity-Modulated Humans
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Interobserver variability in organ at risk delineation in head and neck cancer.

Radiation oncology (London, England) Jun 28, 2021
BACKGROUND: In radiotherapy inaccuracy in organ at risk (OAR) delineation can impact treatment plan optimisation and treatment plan evaluation. Brouwer et al. showed significant interobserver variability (IOV) in OAR delineation in head and neck canc...
Prognosis Humans Radiotherapy Planning, Computer-Assisted Head and Neck Neoplasms Radiotherapy, Intensity-Modulated Organs at Risk Observer Variation Artificial Intelligence Radiotherapy Dosage
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Automation in radiotherapy treatment planning: Examples of use in clinical practice and future trends for a complete automated workflow.

Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique Jun 24, 2021
Modern radiotherapy treatment planning is a complex and time-consuming process that requires the skills of experienced users to obtain quality plans. Since the early 2000s, the automation of this planning process has become an important research topi...
Publishing Workflow Organs at Risk Forecasting Feedback Radiotherapy Planning, Computer-Assisted Deep Learning Automation Humans Radiotherapy Dosage Software
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The dosimetric impact of deep learning-based auto-segmentation of organs at risk on nasopharyngeal and rectal cancer.

Radiation oncology (London, England) Jun 23, 2021
PURPOSE: To investigate the dosimetric impact of deep learning-based auto-segmentation of organs at risk (OARs) on nasopharyngeal and rectal cancer.
Radiotherapy, Intensity-Modulated Humans Nasopharyngeal Neoplasms Prognosis Tomography, X-Ray Computed Organs at Risk Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Deep Learning Image Processing, Computer-Assisted Rectal Neoplasms
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Technical Note: Dose prediction for radiation therapy using feature-based losses and One Cycle Learning.

Medical physics Jun 22, 2021
PURPOSE: To present the technical details of the runner-up model in the open knowledge-based planning (OpenKBP) challenge for the dose-volume histogram (DVH) stream. The model was designed to ensure simple and reproducible training, without the neces...
Humans Deep Learning Organs at Risk Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Radiotherapy, Intensity-Modulated
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