Analyzing the TotalSegmentator for facial feature removal in head CT scans.

Background: Facial recognition technology in medical imaging, particularly with head scans, poses privacy risks due to identifiable facial features. This study evaluates the use of facial recognition software in identifying facial features from head CT scans and explores a defacing pipeline using TotalSegmentator to reduce re-identification risks while preserving data integrity for research.

Methods: 1404 high-quality renderings from the UCLH EIT Stroke dataset, both with and without defacing were analysed.

Metadata-independent classification of MRI sequences using convolutional neural networks: Successful application to prostate MRI.

Purpose: The ever-increasing volume of medical imaging data and interest in Big Data research brings challenges to data organization, categorization, and retrieval. Although the radiological value chain is almost entirely digital, data structuring has been widely performed pragmatically, but with insufficient naming and metadata standards for the stringent needs of image analysis. To enable automated data management independent of naming and metadata, this study focused on developing a convolutional neural network (CNN) that classifies medical images based solely on voxel data.

External validation of the diagnostic value of perihematomal edema characteristics in neoplastic and non-neoplastic intracerebral hemorrhage.

Background And Purpose: Neoplastic intracerebral hemorrhage (ICH) may be incorrectly identified as non-neoplastic ICH on imaging. Relative perihematomal edema (relPHE) on computed tomography (CT) has been proposed as a marker to discriminate neoplastic from non-neoplastic ICH but has not been externally validated. The purpose of this study was to evaluate the discriminatory power of relPHE in an independent cohort.