Background Supplemental screening with MRI has proved beneficial in women with extremely dense breasts. Most MRI examinations show normal anatomic and physiologic variation that may not require radiologic review. Thus, ways to triage these normal MRI...
Computational and mathematical methods in medicine
Oct 4, 2021
The task of segmenting cytoplasm in cytology images is one of the most challenging tasks in cervix cytological analysis due to the presence of fuzzy and highly overlapping cells. Deep learning-based diagnostic technology has proven to be effective in...
At its core, radiation therapy (RT) requires balancing therapeutic effects against risk of adverse events in cancer survivors. The radiation oncologist weighs numerous disease and patient-level factors when considering the expected risk-benefit ratio...
OBJECTIVES: To undertake the first systematic review examining the performance of artificial intelligence (AI) applied to cross-sectional imaging for the diagnosis of acquired pulmonary arterial hypertension (PAH).
BACKGROUND: To estimate median liver iron concentration (LIC) calculated from magnetic resonance imaging, excluded vessels of the liver parenchyma region were defined manually. Previous works proposed the automated method for excluding vessels from t...
Computational and mathematical methods in medicine
Sep 28, 2021
Medical image quality is highly relative to clinical diagnosis and treatment, leading to a popular research topic of medical image denoising. Image denoising based on deep learning methods has attracted considerable attention owing to its excellent a...
Automatic methods for feature extraction, volumetry, and morphometric analysis in clinical neuroscience typically operate on images obtained with magnetic resonance (MR) imaging equipment. Although CT scans are less expensive to acquire and more wide...
This study attempts to explore the radiomics-based features of multi-parametric magnetic resonance imaging (MRI) and construct a machine-learning model to predict the blood supply in vestibular schwannoma preoperatively. By retrospectively collecting...
Background Patients who undergo surgery for cervical radiculopathy are at risk for developing adjacent segment disease (ASD). Identifying patients who will develop ASD remains challenging for clinicians. Purpose To develop and validate a deep learnin...
AJR. American journal of roentgenology
Sep 15, 2021
Shoulder MRI using standard multiplanar sequences requires long scan times. Accelerated sequences have tradeoffs in noise and resolution. Deep learning-based reconstruction (DLR) may allow reduced scan time with preserved image quality. The purpose...