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

In this data note, we present a sorted pool of fetal magnetic resonance imaging (MRI) specimens. These were selected for a project seeking to further develop computer vision software called MaZda, which was 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 final 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.

MaZda software (http://www.eletel.p.lodz.pl/programy/mazda/)makers and developers have shown that they are listening to their users, by continuing to release updates and new versions 1,2 .The samples provided with the manuscript herein were collected and sorted especially for testing upcoming versions of MaZda.The aim is to continue to collaborate with MaZda software engineers in order to code target-recognition semantics and eventually build ideal algorithms for automatic segmentation of prenatal brain.It is important to also note that it is not an easy task to deconstruct the scientific knowledge acquired by radiologists after several hours of practice to master the skills of diagnostic imaging.Moreover, we have recently tested MaZda version 4.6 and version 5.0 and made some recommendations to the software engineers [3][4][5] .In reaction to our need, the MaZda team announced an upcoming version called qMaZda, being co-developed with Weka (www.eletel.p.lodz.pl/pms/SoftwareQmazda.html;www.cs.waikato.ac.nz/ml/weka).We are expecting to see some improvements in qMaZda.

Methods
This dataset was created to improve the efficacy of MaZda.Sample collection was approved by the Research Ethics Committee of the Medical University of Lodz (permit number: RNN/213/13/ KE).Subjects were informed with a written statement of consent for research and publication.As per agreement, personal information was removed from the original specimens.In terms of subject demographics and phenotypes, the background of the patients was consistent with the majority of the Polish population.In 2015, the World Health Organization (WHO) reported 2.68 million neonatal deaths (WHO fact sheet on congenital anomalies, updated September 2016: www.who.int/mediacentre/factsheets/fs370/en/).The estimate of children born with at least one congenital malformation is about 2-3% worldwide (www.who.int/genomics/anomalies/en/Chapter02.pdf).In Poland, the prevalence rate of birth defects was estimated at 52-53 per 1000 live births (http:// www.marchofdimes.org/materials/global-report-on-birth-defectsthe-hidden-toll-of-dying-and-disabled-children-full-report.pdf).Known birth defects can be detected early in pregnancy using noninvasive and/or invasive techniques [6][7][8][9] .Some can be even treated in-utero 10,11 .There flows the rationale behind this collation of fetal magnetic resonance imaging (MRI) data to improve the efficacy of MaZda.
The enrolled subjects underwent MRI examination for the purpose of investigating suspected congenital, obstetrical and placental anomalies that could not be detected by routine ultrasound and genetic amniocentesis.Volunteers who donated fetal MRI samples to create this dataset were in need of fetal, obstetrical or placental care.The criteria for inclusion and exclusion were as follows: 1) 1.5T or 3T MRI; 2) all three anatomical planes were scanned (axial, coronal, sagittal); 3) individual cross-sectional images are "usable" -i.e.not heavily degraded by noise and artifacts, as well as motion blur, due to uncontrollable movement of fetal head; 4) visible fetal brain with no significant malformation; 5) thalamus, grey matter, white matter, and ventricles are also visible (Figure 1).The request to collect MRI scans was sent long after MRI examination was performed.Hence, MRI examination was not prescribed for the purpose of creating this dataset.After looking at 1358 MRI scans in two teleradiology databases, we manually selected 6 patients who had undergone This article is included in the gateway.INCFThis article is included in the Data: Use and Reuse collection.

Introduction
Sample sorting can be useful for clinical research seeking to measure the feasibility of new ideas and to develop new technology.
52ectro-radiology technicians performed fetal MRI as per details provided on the prescription and hospital regulations12.Hence, we did not have control of MR scanners settings, for example.The technicians stored the MRIs on compact discs (CD).By default, MaZda version 4.6 and 5 are lacking an automatic samplesort (sorting algorithm) to extract images and arrange them into folders and subfolders.An option was to create a plugin especially written for MaZda.Due to time consumption, we used other software to carry out image acquisition (Micro Dicom 0.9, Dimensions 2, Sante Dicom 4, Photoshop CS6 64-bit Extended)5.For the extraction of Digital Imaging and Communications in Medicine (DICOM) data, we used the 64-bit portable version of Micro Dicom 0.9.1.Most CDs could be accessed with Micro Dicom or Photoshop CS6 64-bit Extended.We used the rescue feature in Sante Dicom 4 to recover the data and export them in DICOM format.