PURPOSE: To assess the clinical acceptability of a commercial deep-learning-based auto-segmentation (DLAS) prostate model that was retrained using institutional data for delineation of the clinical target volume (CTV) and organs-at-risk (OARs) for po...
Journal of applied clinical medical physics
Mar 15, 2023
BACKGROUND: Recently, target auto-segmentation techniques based on deep learning (DL) have shown promising results. However, inaccurate target delineation will directly affect the treatment planning dose distribution and the effect of subsequent radi...
Clinical oncology (Royal College of Radiologists (Great Britain))
Mar 11, 2023
BACKGROUND AND PURPOSE: Accurate and consistent delineation of cardiac substructures is challenging. The aim of this work was to validate a novel segmentation tool for automatic delineation of cardiac structures and subsequent dose evaluation, with p...
INTRODUCTION: The aim of this review is to describe how various AI-supported applications are used in head and neck cancer radiotherapy treatment planning, and the impact on dose management in regards to target volume and nearby organs at risk (OARs)...
Journal of applied clinical medical physics
Mar 3, 2023
BACKGROUND: Intensity-Modulated Radiation Therapy (IMRT) has been the standard of care for many types of tumors. However, treatment planning for IMRT is a time-consuming and labor-intensive process.
BACKGROUND: Contouring error is one of the top failure modes in radiation treatment. Multiple efforts have been made to develop tools to automatically detect segmentation errors. Deep learning-based auto-segmentation (DLAS) has been used as a baselin...
. The purpose of this study was to evaluate the accuracy of brachytherapy (BT) planning structures derived from Deep learning (DL) based auto-segmentation compared with standard manual delineation for postoperative cervical cancer.. We introduced a c...
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...
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...
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...
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