Prenatal brain MRI samples for development of automatic segmentation, target- recognition and machine-learning algorithms to detect anatomical structures

Abstract

In this data note, we present a sorted pool of fetal magnetic resonance imaging (MRI) specimens, selected for a project seeking to further develop a computer-vision software called MaZda, originally created for magnetic resonance (MR) image analysis. A link to download the samples is provided in the manuscript herein. This data descriptor further explains how and why these fetal MRI samples were selected. Firstly, thousands of cross-sectional images obtained from fetal MRI scans were processed and sorted semi-manually with other software. We did so because a built-in "samplesort" (sorting algorithm) is missing in MaZda version 5. Additionally, the software is unfortunately lacking effective and efficient algorithms to allow automatic identification and segmentation of anatomical structures in fetal MRI samples. Hence, the finals sorting steps were carried out manually via time-consuming methods - i.e. human- visual detection and classifications by the gestational age of pregnancy and the rotational plane of the MR scanner. Thus the latter correlates with the anatomical plane of the mother, rather than the hypothetical plane used to transect the fetus. In brief, we collated these fetal MRI samples in an effort to facilitate future research and discovery, especially to aid the improvement of MaZda.