Nobuyuki Shiraga

TL;DR: The usefulness of the method is demonstrated by the segmentation and visualization of brain vessels from magnetic resonance imaging and magnetic resonance angiography, bronchi from a chest CT, and liver vessels (portal veins) from an abdominal CT.

TL;DR: The usefulness of the proposed tissue classification method is demonstrated by comparisons with conventional single-channel classification using both synthesized data and clinical data acquired with CT (computed tomography) and MRI (magnetic resonance imaging) scanners.

TL;DR: A new visualization method that can clearly depict densitometric as well as geometric information in vascular visualization from 3D data such as obtained from MR and CT angiography is demonstrated.

TL;DR: In this article , the authors used a combination of mean and maximum CT values (cutoffs of - 150 HU and 380 HU for mean CT value and maximum computed tomography (CT) value, respectively) to predict lepidic predominant lesions, with a sensitivity and specificity of 77.8% and 86.2% respectively.

TL;DR: In this paper , a 22-year-old Japanese woman who underwent living-donor kidney transplantation at 3 years due to end-stage renal disease caused by focal segmental glomerulosclerosis (FSGS) presented with a renal transplant AVF (gourd-shaped; 42 × 19 × 20 mm).