AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Organs at Risk

Showing 251 to 260 of 295 articles

Clear Filters

Deep-learning based surface region selection for deep inspiration breath hold (DIBH) monitoring in left breast cancer radiotherapy.

Physics in medicine and biology
Deep inspiration breath hold (DIBH) with surface supervising is a common technique for cardiac dose reduction in left breast cancer radiotherapy. Surface supervision accuracy relies on the characteristics of surface region. In this study, a convoluti...

Technical Note: More accurate and efficient segmentation of organs-at-risk in radiotherapy with convolutional neural networks cascades.

Medical physics
PURPOSE: Manual delineation of organs-at-risk (OARs) in radiotherapy is both time-consuming and subjective. Automated and more accurate segmentation is of the utmost importance in clinical application. The purpose of this study is to further improve ...

A study of positioning orientation effect on segmentation accuracy using convolutional neural networks for rectal cancer.

Journal of applied clinical medical physics
PURPOSE: Convolutional neural networks (CNN) have greatly improved medical image segmentation. A robust model requires training data can represent the entire dataset. One of the differing characteristics comes from variability in patient positioning ...

Assessment of specific versus combined purpose knowledge based models in prostate radiotherapy.

Journal of applied clinical medical physics
Knowledge-based planning (KBP) can be used to improve plan quality, planning speed, and reduce the inter-patient plan variability. KPB may also identify and reduce systematic variations in VMAT plans, something very important in multi-institutional c...

Deep-learning-based detection and segmentation of organs at risk in nasopharyngeal carcinoma computed tomographic images for radiotherapy planning.

European radiology
OBJECTIVE: Accurate detection and segmentation of organs at risks (OARs) in CT image is the key step for efficient planning of radiation therapy for nasopharyngeal carcinoma (NPC) treatment. We develop a fully automated deep-learning-based method (te...

Fully automatic multi-organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks.

Medical physics
PURPOSE: Intensity modulated radiation therapy (IMRT) is commonly employed for treating head and neck (H&N) cancer with uniform tumor dose and conformal critical organ sparing. Accurate delineation of organs-at-risk (OARs) on H&N CT images is thus es...

Development of deep neural network for individualized hepatobiliary toxicity prediction after liver SBRT.

Medical physics
BACKGROUND: Accurate prediction of radiation toxicity of healthy organs-at-risks (OARs) critically determines the radiation therapy (RT) success. The existing dose-volume histogram-based metric may grossly under/overestimate the therapeutic toxicity ...

Knowledge-Based Planning for Identifying High-Risk Stereotactic Ablative Radiation Therapy Treatment Plans for Lung Tumors Larger Than 5 cm.

International journal of radiation oncology, biology, physics
PURPOSE: Stereotactic ablative body radiation therapy (SABR) for lung tumors ≥5 cm can be associated with more toxicity than that for smaller tumors. We investigated the relationship between dosimetry and toxicity and used a knowledge-based planning ...

Creation of knowledge-based planning models intended for large scale distribution: Minimizing the effect of outlier plans.

Journal of applied clinical medical physics
Knowledge-based planning (KBP) can be used to estimate dose-volume histograms (DVHs) of organs at risk (OAR) using models. The task of model creation, however, can result in estimates with differing accuracy; particularly when outlier plans are not p...