BACKGROUND: Preoperative intramuscular fat (IMF) is a strong predictor of tendon failure after a rotator cuff repair. Due to the contemporary labor intensive and time-dependent manual segmentation required for quantitative assessment of IMF, clinical...
BACKGROUND: Magnetic resonance imaging (MRI) is a highly sensitive modality for diagnosing breast cancer, providing an expanding range of clinical usages that are crucial for the care of women at elevated risk of breast cancer development. Segmentati...
PURPOSE: Preeclampsia (PE) is associated with placental insufficiency and could lead to adverse pregnancy outcomes. The study aimed to develop a placental T2-weighted image-based automatic quantitative model for the identification of PE pregnancies a...
BACKGROUND: Alzheimer disease (AD) is a progressive condition characterized by cognitive decline and memory loss. Vision transformers (ViTs) are emerging as promising deep learning models in medical imaging, with potential applications in the detecti...
Biomedical physics & engineering express
Feb 5, 2025
U-Net is widely used in medical image segmentation due to its simple and flexible architecture design. To address the challenges of scale and complexity in medical tasks, several variants of U-Net have been proposed. In particular, methods based on V...
Quantification of tissue stiffness with magnetic resonance elastography (MRE) is an inverse problem that is sensitive to noise. Conventional methods for the purpose include direct inversion (DI) and local frequency estimation (LFE). In this study, we...
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Feb 4, 2025
BACKGROUND AND PURPOSE: Magnetic resonance-guided adaptive radiotherapy (MRgART) may improve the efficacy of large brain metastases (BMs)(≥2 cm), whereas the workflow requires optimized. This study develops a two-stage, personalized deep learning aut...
OBJECTIVES: To develop a deep learning model based on the You Only Look Once version 10 (YOLOv10) for detecting early-stage ONFH in adult using radiographs.
RATIONALE AND OBJECTIVES: This study evaluated StarGAN, a deep learning model designed to generate synthetic computed tomography (sCT) images from magnetic resonance imaging (MRI) and cone-beam computed tomography (CBCT) data using a single model. Th...
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