DLLabelsCT: Annotation tool using deep transfer learning to assist in creating new datasets from abdominal computed tomography scans, case study: Pancreas.

The utilization of artificial intelligence (AI) is expanding significantly within medical research and, to some extent, in clinical practice. Deep learning (DL) applications, which use large convolutional neural networks (CNN), hold considerable potential, especially in optimizing radiological evaluations. However, training DL algorithms to clinical standards requires extensive datasets, and their processing is labor-intensive.

Photon-counting in dual-contrast-enhanced computed tomography: a proof-of-concept quantitative biomechanical assessment of articular cartilage.

This proof-of-concept study explores quantitative imaging of articular cartilage using photon-counting detector computed tomography (PCD-CT) with a dual-contrast agent approach, comparing it to clinical dual-energy CT (DECT). The diffusion of cationic iodinated CA4 + and non-ionic gadolinium-based gadoteridol contrast agents into ex vivo bovine medial tibial plateau cartilage was tracked over 72 h. Continuous maps of the contrast agents' diffusion were created, and correlations with biomechanical indentation parameters (equilibrium and instantaneous moduli, and relaxation time constants) were examined at 28 specific locations.

Imaging phenotype evaluation from digital breast tomosynthesis data: A preliminary study.

Background: Digital breast tomosynthesis (DBT) has been widely adopted as a supplemental imaging modality for diagnostic evaluation of breast cancer and confirmation studies. In this study, a deep learning-based method for characterizing breast tissue patterns in DBT data is presented.

Methods: A set of 5388 2D image patches was produced from 230 right mediolateral oblique, 259 left mediolateral oblique, 18 right craniocaudal, and 15 left craniocaudal single-breast DBT studies, using slice-wise annotations of abnormalities and normal tissue.