Evaluation of artificial intelligence-based autosegmentation for a high-performance cone-beam computed tomography imaging system in the pelvic region.

Background And Purpose: A novel ring-gantry cone-beam computed tomography (CBCT) imaging system shows improved image quality compared to its conventional version, but its effect on autosegmentation is unknown. This study evaluates the impact of this high-performance CBCT on autosegmentation performance, inter-observer variability, contour correction times and delineation confidence, compared to the conventional CBCT.

Materials And Methods: Twenty prostate cancer patients were enrolled in this prospective clinical study.

Generation and characterization of novel induced pluripotent stem cell (iPSC) lines derived from three symptomatic carriers of a pathogenic MYH11 variant and two non-carrier relatives.

A novel pathogenic variant in the MYH11 gene (c.4559+1G>A) leading to exon 32 skipping, is a rare cause of familial aortic aneurysms and dissections (fTAAD). The phenotype has proven highly variable with reduced penetrance.

Minimally interactive segmentation of soft-tissue tumors on CT and MRI using deep learning.

Background: Segmentations are crucial in medical imaging for morphological, volumetric, and radiomics biomarkers. Manual segmentation is accurate but not feasible in clinical workflow, while automatic segmentation generally performs sub-par.

Purpose: To develop a minimally interactive deep learning-based segmentation method for soft-tissue tumors (STTs) on CT and MRI.