AIMC Topic: Organs at Risk

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Implementation of deep learning-based auto-segmentation for radiotherapy planning structures: a workflow study at two cancer centers.

Radiation oncology (London, England) Jun 8, 2021
PURPOSE: We recently described the validation of deep learning-based auto-segmented contour (DC) models for organs at risk (OAR) and clinical target volumes (CTV). In this study, we evaluate the performance of implemented DC models in the clinical ra...
Head and Neck Neoplasms Prostatic Neoplasms Workflow Deep Learning Image Processing, Computer-Assisted Prognosis Tomography, X-Ray Computed Male Humans Radiotherapy Dosage Algorithms Radiotherapy, Intensity-Modulated Central Nervous System Neoplasms Organs at Risk Radiotherapy Planning, Computer-Assisted Health Plan Implementation
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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) May 13, 2021
Deep learning has revolutionized image processing and achieved the-state-of-art performance in many medical image segmentation tasks. Many deep learning-based methods have been published to segment different parts of the body for different medical ap...
Organs at Risk Head Image Processing, Computer-Assisted Head and Neck Neoplasms Humans Deep Learning
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Metrics to evaluate the performance of auto-segmentation for radiation treatment planning: A critical review.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology May 11, 2021
Advances in artificial intelligence-based methods have led to the development and publication of numerous systems for auto-segmentation in radiotherapy. These systems have the potential to decrease contour variability, which has been associated with ...
Humans Radiotherapy Planning, Computer-Assisted Radiometry Artificial Intelligence Benchmarking Organs at Risk
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A deep learning-based auto-segmentation system for organs-at-risk on whole-body computed tomography images for radiation therapy.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology May 4, 2021
BACKGROUND AND PURPOSE: Delineating organs at risk (OARs) on computed tomography (CT) images is an essential step in radiation therapy; however, it is notoriously time-consuming and prone to inter-observer variation. Herein, we report a deep learning...
Humans Radiotherapy Planning, Computer-Assisted Image Processing, Computer-Assisted Head and Neck Neoplasms Organs at Risk Tomography, X-Ray Computed Deep Learning
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DVHnet: A deep learning-based prediction of patient-specific dose volume histograms for radiotherapy planning.

Medical physics Apr 18, 2021
PURPOSE: To develop a deep learning method to predict patient-specific dose volume histograms (DVHs) for radiotherapy planning.
Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Radiotherapy, Intensity-Modulated Nasopharyngeal Neoplasms Humans Deep Learning Organs at Risk
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Deep learning-based auto-segmentation of organs at risk in high-dose rate brachytherapy of cervical cancer.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Apr 6, 2021
BACKGROUND AND PURPOSE: Delineation of organs at risk (OARs), such as the bladder, rectum and sigmoid, plays an important role in the delivery of optimal absorbed dose to the target owing to the steep gradient in high-dose rate brachytherapy (HDR-BT)...
Organs at Risk Brachytherapy Humans Female Uterine Cervical Neoplasms Reproducibility of Results Deep Learning Image Processing, Computer-Assisted
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Evaluation of a highly refined prediction model in knowledge-based volumetric modulated arc therapy planning for cervical cancer.

Radiation oncology (London, England) Mar 22, 2021
BACKGROUND AND PURPOSE: To explore whether a highly refined dose volume histograms (DVH) prediction model can improve the accuracy and reliability of knowledge-based volumetric modulated arc therapy (VMAT) planning for cervical cancer.
Uterine Cervical Neoplasms Radiotherapy Planning, Computer-Assisted Humans Knowledge Bases Female Radiotherapy, Intensity-Modulated Organs at Risk
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Deep learning dose prediction for IMRT of esophageal cancer: The effect of data quality and quantity on model performance.

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) Mar 10, 2021
PURPOSE: To investigate the effect of data quality and quantity on the performance of deep learning (DL) models, for dose prediction of intensity-modulated radiotherapy (IMRT) of esophageal cancer.
Humans Retrospective Studies Deep Learning Esophageal Neoplasms Organs at Risk Data Accuracy Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Radiotherapy, Intensity-Modulated
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Clinical feasibility of deep learning-based auto-segmentation of target volumes and organs-at-risk in breast cancer patients after breast-conserving surgery.

Radiation oncology (London, England) Feb 25, 2021
BACKGROUND: In breast cancer patients receiving radiotherapy (RT), accurate target delineation and reduction of radiation doses to the nearby normal organs is important. However, manual clinical target volume (CTV) and organs-at-risk (OARs) segmentat...
Middle Aged Humans Deep Learning Feasibility Studies Tomography, X-Ray Computed Radiotherapy, Intensity-Modulated Organs at Risk Female Adult Radiometry Radiotherapy Planning, Computer-Assisted Aged Breast Neoplasms Observer Variation Mastectomy, Segmental
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Self-channel-and-spatial-attention neural network for automated multi-organ segmentation on head and neck CT images.

Physics in medicine and biology Dec 11, 2020
Accurate segmentation of organs at risk (OARs) is necessary for adaptive head and neck (H&N) cancer treatment planning, but manual delineation is tedious, slow, and inconsistent. A self-channel-and-spatial-attention neural network (SCSA-Net) is devel...
Organs at Risk Tomography, X-Ray Computed Humans Image Processing, Computer-Assisted Neural Networks, Computer Head and Neck Neoplasms
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