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Observer Variation

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Deep learning-based segmentation of the thorax in mouse micro-CT scans.

Scientific reports
For image-guided small animal irradiations, the whole workflow of imaging, organ contouring, irradiation planning, and delivery is typically performed in a single session requiring continuous administration of anaesthetic agents. Automating contourin...

Artificial intelligence-assisted interpretation of Ki-67 expression and repeatability in breast cancer.

Diagnostic pathology
BACKGROUND: Ki-67 standard reference card (SRC) and artificial intelligence (AI) software were used to evaluate breast cancer Ki-67LI. We established training and validation sets and studied the repeatability inter-observers.

Automatic Assessment of Pectus Excavatum Severity From CT Images Using Deep Learning.

IEEE journal of biomedical and health informatics
Pectus excavatum (PE) is the most common abnormality of the thoracic cage, whose severity is evaluated by extracting three indices (Haller, correction and asymmetry) from computed tomography (CT) images. To date, this analysis is performed manually, ...

Machine Learning for Auto-Segmentation in Radiotherapy Planning.

Clinical oncology (Royal College of Radiologists (Great Britain))
Manual segmentation of target structures and organs at risk is a crucial step in the radiotherapy workflow. It has the disadvantages that it can require several hours of clinician time per patient and is prone to inter- and intra-observer variability...

Artificial intelligence for the assessment of bowel preparation.

Gastrointestinal endoscopy
BACKGROUND AND AIMS: A reliable assessment of bowel preparation is important to ensure high-quality colonoscopy. Current bowel preparation scoring systems are limited by interobserver variability. This study aimed to demonstrate objective assessment ...

Deep-learning model observer for a low-contrast hepatic metastases localization task in computed tomography.

Medical physics
PURPOSE: Conventional model observers (MO) in CT are often limited to a uniform background or varying background that is random and can be modeled in an analytical form. It is unclear if these conventional MOs can be readily generalized to predict hu...

Deep learning-based GTV contouring modeling inter- and intra- observer variability in sarcomas.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: The delineation of the gross tumor volume (GTV) is a critical step for radiation therapy treatment planning. The delineation procedure is typically performed manually which exposes two major issues: cost and reproducibility. D...

Application of deep learning to auto-delineation of target volumes and organs at risk in radiotherapy.

Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
The technological advancement heralded the arrival of precision radiotherapy (RT), thereby increasing the therapeutic ratio and decreasing the side effects from treatment. Contour of target volumes (TV) and organs at risk (OARs) in RT is a complicate...

Comparing deep learning-based automatic segmentation of breast masses to expert interobserver variability in ultrasound imaging.

Computers in biology and medicine
Deep learning is a powerful tool that became practical in 2008, harnessing the power of Graphic Processing Unites, and has developed rapidly in image, video, and natural language processing. There are ongoing developments in the application of deep l...