Structured Reporting of Solid and Cystic Pancreatic Lesions in CT and MRI: Consensus-Based Structured Report Templates of the German Society of Radiology (DRG)

 Radiological reports of pancreatic lesions are currently widely formulated as free texts. However, for optimal characterization, staging and operation planning, a wide range of information is required but is sometimes not captured comprehensively. Structured reporting offers the potential for improvement in terms of completeness, reproducibility and clarity of interdisciplinary communication. Interdisciplinary consensus finding of structured report templates for solid and cystic pancreatic tumors in computed tomography (CT) and magnetic resonance imaging (MRI) with representatives of the German Society of Radiology (DRG), German Society for General and Visceral Surgery (DGAV), working grou…

Verwendung eines 3D Neuronalen Netzwerkes zur Lebervolumenbestimmmung im 3T MRT

A 3D Deep Neural Network for Liver Volumetry in 3T Contrast-Enhanced MRI.

 To create a fully automated, reliable, and fast segmentation tool for Gd-EOB-DTPA-enhanced MRI scans using deep learning. Datasets of Gd-EOB-DTPA-enhanced liver MR images of 100 patients were assembled. Ground truth segmentation of the hepatobiliary phase images was performed manually. Automatic image segmentation was achieved with a deep convolutional neural network. Our neural network achieves an intraclass correlation coefficient (ICC) of 0.987, a Sørensen-Dice coefficient of 96.7 ± 1.9 % (mean ± std), an overlap of 92 ± 3.5 %, and a Hausdorff distance of 24.9 ± 14.7 mm compared with two expert readers who corresponded to an ICC of 0.973, a Sørensen-Dice coefficient of 95.2 ± 2.8 %, and…