Metastatic Lung Lesion Changes in Follow-up Chest CT: The Advantage of Deep Learning Simultaneous Analysis of Prior and Current Scans With SimU-Net.

Purpose: Radiological follow-up of oncology patients requires the detection of metastatic lung lesions and the quantitative analysis of their changes in longitudinal imaging studies. Our aim was to evaluate SimU-Net, a novel deep learning method for the automatic analysis of metastatic lung lesions and their temporal changes in pairs of chest CT scans.

Materials And Methods: SimU-Net is a simultaneous multichannel 3D U-Net model trained on pairs of registered prior and current scans of a patient.

Fetal body composition reference charts and sexual dimorphism using magnetic resonance imaging.

Background: The American Academy of Pediatrics advises that the nutrition of preterm infants should target a body composition similar to that of a fetus in utero. Still, reference charts for intrauterine body composition are missing. Moreover, data on sexual differences in intrauterine body composition during pregnancy are limited.

Measuring Geographic Atrophy Area Using Column-Based Machine Learning Software on Spectral-Domain Optical Coherence Tomography versus Fundus Auto Fluorescence.

Background: The purpose of this study was to compare geographic atrophy (GA) area semi-automatic measurement using fundus autofluorescence (FAF) versus optical coherence tomography (OCT) annotation with the cRORA (complete retinal pigment epithelium and outer retinal atrophy) criteria.

Methods: GA findings on FAF and OCT were semi-automatically annotated at a single time point in 36 pairs of FAF and OCT scans obtained from 36 eyes in 24 patients with dry age-related macular degeneration (AMD). The GA area, focality, perimeter, circularity, minimum and maximum Feret diameter, and minimum distance from the center were compared between FAF and OCT annotations.