AIMC Topic: Organs at Risk

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Segmentation of multiple Organs-at-Risk associated with brain tumors based on coarse-to-fine stratified networks.

Medical physics Feb 16, 2023
BACKGROUND: Delineation of Organs-at-Risks (OARs) is an important step in radiotherapy treatment planning. As manual delineation is time-consuming, labor-intensive and affected by inter- and intra-observer variability, a robust and efficient automati...
Humans Neural Networks, Computer Tomography, X-Ray Computed Organs at Risk Radiotherapy Planning, Computer-Assisted Brain Neoplasms Image Processing, Computer-Assisted
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A novel mathematical model to generate semi-automated optimal IMRT treatment plan based on predicted 3D dose distribution and prescribed dose.

Medical physics Feb 2, 2023
BACKGROUND: In recent years, with the development of artificial intelligence and deep learning techniques, it has become possible to predict the three-dimensional distribution dose (3D ) of a new patient based on the treatment plans of similar recent...
Humans Artificial Intelligence Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Radiotherapy, Intensity-Modulated Models, Theoretical Organs at Risk
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Automatic segmentation of kidneys in computed tomography images using U-Net.

Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique Feb 2, 2023
PURPOSE: Accurate segmentation of target volumes and organs at risk from computed tomography (CT) images is essential for treatment planning in radiation therapy. The segmentation task is often done manually making it time-consuming. Besides, it is b...
Humans Organs at Risk Image Processing, Computer-Assisted Tomography, X-Ray Computed Artificial Intelligence Kidney Radiotherapy Planning, Computer-Assisted
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Deep Learning-Based Dose Prediction for Automated, Individualized Quality Assurance of Head and Neck Radiation Therapy Plans.

Practical radiation oncology Jan 24, 2023
PURPOSE: This study aimed to use deep learning-based dose prediction to assess head and neck (HN) plan quality and identify suboptimal plans.
Organs at Risk Radiotherapy Planning, Computer-Assisted Humans Deep Learning Radiotherapy Dosage Radiotherapy, Intensity-Modulated
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A clinical and time savings evaluation of a deep learning automatic contouring algorithm.

Medical dosimetry : official journal of the American Association of Medical Dosimetrists Dec 20, 2022
Automatic contouring algorithms may streamline clinical workflows by reducing normal organ-at-risk (OAR) contouring time. Here we report the first comprehensive quantitative and qualitative evaluation, along with time savings assessment for a prototy...
Retrospective Studies Humans Neck Deep Learning Algorithms Organs at Risk Radiotherapy Planning, Computer-Assisted
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Deep learning architecture with transformer and semantic field alignment for voxel-level dose prediction on brain tumors.

Medical physics Dec 5, 2022
PURPOSE: The use of convolution neural networks (CNN) to accurately predict dose distributions can accelerate intensity-modulated radiation therapy (IMRT) planning. The purpose of our study is to develop a novel deep learning architecture for precise...
Organs at Risk Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Radiotherapy, Intensity-Modulated Semantics Brain Neoplasms Deep Learning Humans Retrospective Studies
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Improved accuracy of auto-segmentation of organs at risk in radiotherapy planning for nasopharyngeal carcinoma based on fully convolutional neural network deep learning.

Oral oncology Nov 26, 2022
OBJECTIVE: We examined a modified encoder-decoder architecture-based fully convolutional neural network, OrganNet, for simultaneous auto-segmentation of 24 organs at risk (OARs) in the head and neck, followed by validation tests and evaluation of cli...
Organs at Risk Radiotherapy Planning, Computer-Assisted Nasopharyngeal Neoplasms Deep Learning Nasopharyngeal Carcinoma Humans Image Processing, Computer-Assisted Neural Networks, Computer
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Patient-specific transfer learning for auto-segmentation in adaptive 0.35 T MRgRT of prostate cancer: a bi-centric evaluation.

Medical physics Nov 7, 2022
BACKGROUND: Online adaptive radiation therapy (RT) using hybrid magnetic resonance linear accelerators (MR-Linacs) can administer a tailored radiation dose at each treatment fraction. Daily MR imaging followed by organ and target segmentation adjustm...
Machine Learning Tomography, X-Ray Computed Image Processing, Computer-Assisted Prostatic Neoplasms Humans Organs at Risk Male Artificial Intelligence Radiotherapy Planning, Computer-Assisted
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Deep learning empowered volume delineation of whole-body organs-at-risk for accelerated radiotherapy.

Nature communications Nov 2, 2022
In radiotherapy for cancer patients, an indispensable process is to delineate organs-at-risk (OARs) and tumors. However, it is the most time-consuming step as manual delineation is always required from radiation oncologists. Herein, we propose a ligh...
Image Processing, Computer-Assisted Tomography, X-Ray Computed Organs at Risk Humans Neoplasms Deep Learning
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Comparison of atlas-based and deep learning methods for organs at risk delineation on head-and-neck CT images using an automated treatment planning system.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Nov 1, 2022
BACKGROUND AND PURPOSE: To investigate the performance of head-and-neck (HN) organs-at-risk (OAR) automatic segmentation (AS) using four atlas-based (ABAS) and two deep learning (DL) solutions.
Head and Neck Neoplasms Organs at Risk Tomography, X-Ray Computed Deep Learning Humans Radiotherapy Planning, Computer-Assisted
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