MR images of fetuses allow clinicians to detect brain abnormalities in an early stage of development. The cornerstone of volumetric and morphologic analysis in fetal MRI is segmentation of the fetal brain into different tissue classes. Manual segmentation is cumbersome and time consuming, hence automatic segmen-method, Dice coefficient (DC) and Mean surface distance (MSD) per tissue class were computed between automatic and manual expert annotations. When the training data was enriched by simulated intensity inhomogeneity artifacts, the average achieved DC over all tissue classes and images increased from 0.77 to 0.88, and MSD decreased from 0.78 mm to 0.37 mm. These results demonstrate that the proposed approach can potentially replace or complement preprocessing steps, such as bias field corrections, and thereby improve the segmentation performance.

No abstract

BACKGROUND AND PURPOSE:Fetuses and neonates with critical congenital heart disease are at risk of delayed brain development and neurodevelopmental impairments. Our aim was to investigate the association between fetal and neonatal brain volumes and neonatal brain injury in a longitudinally scanned cohort with an antenatal diagnosis of critical congenital heart disease and to relate fetal and neonatal brain volumes to postmenstrual age and type of congenital heart disease. MATERIALS AND METHODS:This was a prospective, longitudinal study including 61 neonates with critical congenital heart disease undergoing surgery with cardiopulmonary bypass Ͻ30 days after birth and MR imaging of the brain; antenatally (33 weeks postmenstrual age), neonatal preoperatively (first week), and postoperatively (7 days postoperatively). Twenty-six had 3 MR imaging scans; 61 had at least 1 fetal and/or neonatal MR imaging scan. Volumes (cubic centimeters) were calculated for total brain volume, unmyelinated white matter, cortical gray matter, cerebellum, extracerebral CSF, and ventricular CSF. MR images were reviewed for ischemic brain injury. RESULTS:Total fetal brain volume, cortical gray matter, and unmyelinated white matter positively correlated with preoperative neonatal total brain volume, cortical gray matter, and unmyelinated white matter (r ϭ 0.5-0.58); fetal ventricular CSF and extracerebral CSF correlated with neonatal ventricular CSF and extracerebral CSF (r ϭ 0.64 and 0.82). Fetal cortical gray matter, unmyelinated white matter, and the cerebellum were negatively correlated with neonatal ischemic injury (r ϭ Ϫ0.46 to Ϫ0.41); fetal extracerebral CSF and ventricular CSF were positively correlated with neonatal ischemic injury (r ϭ 0.40 and 0.23). Unmyelinated white matter:total brain volume ratio decreased with increasing postmenstrual age, with a parallel increase of cortical gray matter:total brain volume and cerebellum:total brain volume. Fetal ventricular CSF:intracranial volume and extracerebral CSF:intracranial volume ratios decreased with increasing postmenstrual age; however, neonatal ventricular CSF:intracranial volume and extracerebral CSF:intracranial volume ratios increased with postmenstrual age. CONCLUSIONS:This study reveals that fetal brain volumes relate to neonatal brain volumes in critical congenital heart disease, with a negative correlation between fetal brain volumes and neonatal ischemic injury. Fetal brain imaging has the potential to provide early neurologic biomarkers.

Background: There is accumulating evidence showing beneficial effects of early intervention on posthemorrhagic ventricular dilatation (PHVD) and neurologic outcomes. Objective:To compare the effect of early and late intervention for PHVD on brain injury and ventricular volume using term-equivalent age magnetic resonance imaging (TEA-MRI). Methods:In the ELVIS (Early versus Late Ventricular Intervention Study) trial 126 preterm infants ≤34 weeks gestation with PHVD after grade III-IV intraventricular hemorrhage were randomised to low threshold (LT, ventricular index (VI) > p97 and anterior horn width (AHW) > 6mm) or higher threshold (HT, VI > p97 + 4mm and AHW > 10mm). The Kidokoro Global Brain Abnormality Score and the frontal and occipital horn (FOH) ratio were measured. Automatic segmentation was used to perform volumetric measurements. Results:Of the 110 surviving infants, TEA-MRI was obtained in 88 (80%) infants, of which 44 were in the LT group and 44 in the HT group. The total Kidokoro score of the infants in the LT group was lower than in the HT group (median (interquartile range): 8 (5-12) vs 12 (9-17), respectively; p<0.001). When the groups were compared in terms of severity of the Kidokoro score, there were more infants in the LT group with a normal or mildly increased score, and more infants in the HT group with a moderately or severely increased score (46% vs. 11% and 89% vs. 54%, respectively; p=0.002). The FOH ratio was lower in the LT group, when compared with the HT group (0.42 (0.34-0.63) vs 0.48 (0.37-0.68), respectively; p=0.001). The ventricular CSF volumes were calculated in 47 infants and were smaller in the LT group (p=0.03). Conclusion:Our findings demonstrate more brain injury and larger ventricular CSF volumes in the HT group. These results contribute to the growing evidence for the positive effects of early intervention for PHVD.

Automatic neonatal brain tissue segmentation in preterm born infants is a prerequisite for evaluation of brain development. However, automatic segmentation is often hampered by motion artifacts caused by infant head movements during image acquisition. Methods have been developed to remove or minimize these artifacts during image reconstruction using frequency domain data. However, frequency domain data might not always be available. Hence, in this study we propose a method for removing motion artifacts from the already reconstructed MR scans. The method employs a generative adversarial network trained with a cycle consistency loss to transform slices affected by motion into slices without motion artifacts, and vice versa. In the experiments 40 T2weighted coronal MR scans of preterm born infants imaged at 30 weeks postmenstrual age were used. All images contained slices affected by motion artifacts hampering automatic tissue segmentation. To evaluate whether correction allows more accurate image segmentation, the images were segmented into 8 tissue classes: cerebellum, myelinated white matter, basal ganglia and thalami, ventricular cerebrospinal fluid, white matter, brain stem, cortical gray matter, and extracerebral cerebrospinal fluid. Images corrected for motion and corresponding segmentations were qualitatively evaluated using 5-point Likert scale. Before the correction of motion artifacts, median image quality and quality of corresponding automatic segmentations were assigned grade 2 (poor) and 3 (moderate), respectively. After correction of motion artifacts, both improved to grades 3 and 4, respectively. The results indicate that correction of motion artifacts in the image space using the proposed approach allows accurate segmentation of brain tissue classes in slices affected by motion artifacts.

MR images of the fetus allow non-invasive analysis of the fetal brain. Quantitative analysis of fetal brain development requires automatic brain tissue segmentation that is typically preceded by segmentation of the intracranial volume (ICV). This is challenging because fetal MR images visualize the whole moving fetus and in addition partially visualize the maternal body. This paper presents an automatic method for segmentation of the ICV in fetal MR images. The method employs a multi-scale convolutional neural network in 2D slices to enable learning spatial information from larger context as well as detailed local information. The method is developed and evaluated with 30 fetal T2-weighted MRI scans (average age 33.2±1.2 weeks postmenstrual age). The set contains 10 scans acquired in axial, 10 in coronal and 10 in sagittal imaging planes. A reference standard was defined in all images by manual annotation of the intracranial volume in 10 equidistantly distributed slices. The automatic analysis was performed by training and testing the network using scans acquired in the representative imaging plane as well as combining the training data from all imaging planes. On average, the automatic method achieved Dice coefficients of 0.90 for the axial images, 0.90 for the coronal images and 0.92 for the sagittal images. Combining the training sets resulted in average Dice coefficients of 0.91 for the axial images, 0.95 for the coronal images, and 0.92 for the sagittal images. The results demonstrate that the evaluated method achieved good performance in extracting ICV in fetal MR scans regardless of the imaging plane.

No abstract

HighlightsAutomatic intracranial volume segmentation.Fetal and neonatal MRI.Deep learning.