Anatomical variations occur during head and neck (H&N) radiotherapy (RT) treatment.
These variations may result in underdosage to the target volume or overdosage to the organ …

Objective: The objective of this review was to provide an overview of the diverse methods
described, tested, or implemented for monitoring performance of clinical artificial intelligence …

Purpose/objective (s) Auto-segmentation with artificial intelligence (AI) offers an opportunity
to reduce inter-and intra-observer variability in contouring, to improve the quality of contours …

Objective. The output of a deep learning (DL) auto-segmentation application should be
reviewed, corrected if needed and approved before being used clinically. This verification …

Background and purpose Existing methods for quality assurance of the radiotherapy auto-
segmentations focus on the correlation between the average model entropy and the Dice …

Background and Purpose A wide range of quantitative measures are available to facilitate
clinical implementation of auto-contouring software, on-going Quality Assurance (QA) and …

Purpose The large variability in tumor appearance and shape makes manual delineation of
the clinical target volume (CTV) time-consuming, and the results depend on the oncologists' …

Purpose/Objective (s): With the development of deep learning, more convolutional neural
networks (CNNs) are being introduced in automatic segmentation to reduce oncologists' …

Purpose One-table treatments with treatment imaging, preparation and delivery occurring at
one treatment couch, could increase patients' comfort and throughput for palliative …

Background: Chest X-ray (CXR) imaging is the most common examination; however, no
automatic quality assurance (QA) system using deep learning (DL) has been established for …