AIMC Topic: Organs at Risk

Clear Filters Showing 161 to 170 of 326 articles

Registration-guided deep learning image segmentation for cone beam CT-based online adaptive radiotherapy.

Medical physics May 4, 2022
PURPOSE: Adaptive radiotherapy (ART), especially online ART, effectively accounts for positioning errors and anatomical changes. One key component of online ART process is accurately and efficiently delineating organs at risk (OARs) and targets on on...
Humans Algorithms Image Processing, Computer-Assisted Organs at Risk Radiotherapy Planning, Computer-Assisted Deep Learning Cone-Beam Computed Tomography
View on PubMed DOI

Clinical evaluation of a deep learning model for segmentation of target volumes in breast cancer radiotherapy.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Apr 18, 2022
PURPOSE/OBJECTIVE(S): Precise segmentation of clinical target volumes (CTV) in breast cancer is indispensable for state-of-the art radiotherapy. Despite international guidelines, significant intra- and interobserver variability exists, negatively imp...
Radiotherapy Planning, Computer-Assisted Humans Image Processing, Computer-Assisted Breast Neoplasms Female Organs at Risk Neural Networks, Computer Deep Learning
View on PubMed DOI

Artificial Intelligence Radiotherapy Planning: Automatic Segmentation of Human Organs in CT Images Based on a Modified Convolutional Neural Network.

Frontiers in public health Apr 15, 2022
OBJECTIVE: Precise segmentation of human organs and anatomic structures (especially organs at risk, OARs) is the basis and prerequisite for the treatment planning of radiation therapy. In order to ensure rapid and accurate design of radiotherapy trea...
Humans Artificial Intelligence Image Processing, Computer-Assisted Tomography, X-Ray Computed Neural Networks, Computer Organs at Risk
View on PubMed DOI

Dose prediction via distance-guided deep learning: Initial development for nasopharyngeal carcinoma radiotherapy.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Mar 26, 2022
BACKGROUND AND PURPOSE: Geometric information such as distance information is essential for dose calculations in radiotherapy. However, state-of-the-art dose prediction methods use only binary masks without distance information. This study aims to de...
Radiotherapy Dosage Radiotherapy, Intensity-Modulated Humans Organs at Risk Nasopharyngeal Neoplasms Deep Learning Radiotherapy Planning, Computer-Assisted Retrospective Studies Nasopharyngeal Carcinoma
View on PubMed DOI

Deep Learning-Based Automatic Assessment of Radiation Dermatitis in Patients With Nasopharyngeal Carcinoma.

International journal of radiation oncology, biology, physics Mar 15, 2022
PURPOSE: Radiation dermatitis (RD) is a common, unpleasant side effect of patients receiving radiation therapy. In clinical practice, the severity of RD is graded manually through visual inspection, which is labor intensive and often leads to large i...
Nasopharyngeal Carcinoma Nasopharyngeal Neoplasms Radiodermatitis Image Processing, Computer-Assisted Organs at Risk Humans Tomography, X-Ray Computed Deep Learning
View on PubMed DOI

The feasibility study on the generalization of deep learning dose prediction model for volumetric modulated arc therapy of cervical cancer.

Journal of applied clinical medical physics Mar 9, 2022
PURPOSE: To develop a 3D-Unet dose prediction model to predict the three-dimensional dose distribution of volumetric modulated arc therapy (VMAT) for cervical cancer and test the dose prediction performance of the model in endometrial cancer to explo...
Humans Deep Learning Feasibility Studies Organs at Risk Female Uterine Cervical Neoplasms Radiotherapy, Intensity-Modulated Endometrial Neoplasms Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage
View on PubMed DOI

Deep learning-based auto segmentation using generative adversarial network on magnetic resonance images obtained for head and neck cancer patients.

Journal of applied clinical medical physics Mar 9, 2022
PURPOSE: Adaptive radiotherapy requires auto-segmentation in patients with head and neck (HN) cancer. In the current study, we propose an auto-segmentation model using a generative adversarial network (GAN) on magnetic resonance (MR) images of HN can...
Magnetic Resonance Imaging Organs at Risk Image Processing, Computer-Assisted Head and Neck Neoplasms Humans Deep Learning
View on PubMed DOI

Evaluating the clinical acceptability of deep learning contours of prostate and organs-at-risk in an automated prostate treatment planning process.

Medical physics Feb 21, 2022
BACKGROUND: Radiation treatment is considered an effective and the most common treatment option for prostate cancer. The treatment planning process requires accurate and precise segmentation of the prostate and organs at risk (OARs), which is laborio...
Radiotherapy Planning, Computer-Assisted Deep Learning Humans Prostate Organs at Risk Artificial Intelligence Male
View on PubMed DOI

Adopting Machine Learning Auto-Segmentation Into Radiotherapy Workflow.

Clinical oncology (Royal College of Radiologists (Great Britain)) Feb 17, 2022
Organs at Risk Radiotherapy Planning, Computer-Assisted Machine Learning Radiation Oncology Humans Workflow Image Processing, Computer-Assisted
View on PubMed DOI

A hybrid optimization strategy for deliverable intensity-modulated radiotherapy plan generation using deep learning-based dose prediction.

Medical physics Feb 9, 2022
PURPOSE: To propose a clinically feasible automatic planning solution for external beam intensity-modulated radiotherapy, including dose prediction via a deep learning and voxel-based optimization strategy.
Organs at Risk Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated Nasopharyngeal Neoplasms Radiotherapy Dosage Deep Learning China Humans
View on PubMed DOI
Popular Topics
Recent Journals