24 research outputs found
Real-Time Automatic Fetal Brain Extraction in Fetal MRI by Deep Learning
Brain segmentation is a fundamental first step in neuroimage analysis. In the
case of fetal MRI, it is particularly challenging and important due to the
arbitrary orientation of the fetus, organs that surround the fetal head, and
intermittent fetal motion. Several promising methods have been proposed but are
limited in their performance in challenging cases and in real-time
segmentation. We aimed to develop a fully automatic segmentation method that
independently segments sections of the fetal brain in 2D fetal MRI slices in
real-time. To this end, we developed and evaluated a deep fully convolutional
neural network based on 2D U-net and autocontext, and compared it to two
alternative fast methods based on 1) a voxelwise fully convolutional network
and 2) a method based on SIFT features, random forest and conditional random
field. We trained the networks with manual brain masks on 250 stacks of
training images, and tested on 17 stacks of normal fetal brain images as well
as 18 stacks of extremely challenging cases based on extreme motion, noise, and
severely abnormal brain shape. Experimental results show that our U-net
approach outperformed the other methods and achieved average Dice metrics of
96.52% and 78.83% in the normal and challenging test sets, respectively. With
an unprecedented performance and a test run time of about 1 second, our network
can be used to segment the fetal brain in real-time while fetal MRI slices are
being acquired. This can enable real-time motion tracking, motion detection,
and 3D reconstruction of fetal brain MRI.Comment: This work has been submitted to ISBI 201
Abnormal prenatal brain development in Chiari II malformation
IntroductionThe Chiari II is a relatively common birth defect that is associated with open spinal abnormalities and is characterized by caudal migration of the posterior fossa contents through the foramen magnum. The pathophysiology of Chiari II is not entirely known, and the neurobiological substrate beyond posterior fossa findings remains unexplored. We aimed to identify brain regions altered in Chiari II fetuses between 17 and 26 GW.MethodsWe used in vivo structural T2-weighted MRIs of 31 fetuses (6 controls and 25 cases with Chiari II).ResultsThe results of our study indicated altered development of diencephalon and proliferative zones (ventricular and subventricular zones) in fetuses with a Chiari II malformation compared to controls. Specifically, fetuses with Chiari II showed significantly smaller volumes of the diencephalon and significantly larger volumes of lateral ventricles and proliferative zones.DiscussionWe conclude that regional brain development should be taken into consideration when evaluating prenatal brain development in fetuses with Chiari II
A normative spatiotemporal MRI atlas of the fetal brain for automatic segmentation and analysis of early brain growth.
Longitudinal characterization of early brain growth in-utero has been limited by a number of challenges in fetal imaging, the rapid change in size, shape and volume of the developing brain, and the consequent lack of suitable algorithms for fetal brain image analysis. There is a need for an improved digital brain atlas of the spatiotemporal maturation of the fetal brain extending over the key developmental periods. We have developed an algorithm for construction of an unbiased four-dimensional atlas of the developing fetal brain by integrating symmetric diffeomorphic deformable registration in space with kernel regression in age. We applied this new algorithm to construct a spatiotemporal atlas from MRI of 81 normal fetuses scanned between 19 and 39 weeks of gestation and labeled the structures of the developing brain. We evaluated the use of this atlas and additional individual fetal brain MRI atlases for completely automatic multi-atlas segmentation of fetal brain MRI. The atlas is available online as a reference for anatomy and for registration and segmentation, to aid in connectivity analysis, and for groupwise and longitudinal analysis of early brain growth
Optimal method for fetal brain age prediction using multiplanar slices from structural magnetic resonance imaging
The accurate prediction of fetal brain age using magnetic resonance imaging (MRI) may contribute to the identification of brain abnormalities and the risk of adverse developmental outcomes. This study aimed to propose a method for predicting fetal brain age using MRIs from 220 healthy fetuses between 15.9 and 38.7 weeks of gestational age (GA). We built a 2D single-channel convolutional neural network (CNN) with multiplanar MRI slices in different orthogonal planes without correction for interslice motion. In each fetus, multiple age predictions from different slices were generated, and the brain age was obtained using the mode that determined the most frequent value among the multiple predictions from the 2D single-channel CNN. We obtained a mean absolute error (MAE) of 0.125 weeks (0.875 days) between the GA and brain age across the fetuses. The use of multiplanar slices achieved significantly lower prediction error and its variance than the use of a single slice and a single MRI stack. Our 2D single-channel CNN with multiplanar slices yielded a significantly lower stack-wise MAE (0.304 weeks) than the 2D multi-channel (MAE = 0.979
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Prenatal Diagnosis of a Ventral Abdominal Wall Defect
* Abbreviations:
MFCC: : Maternal Fetal Care Center
MRI: : magnetic resonance imaging
VSD: : ventricular septal defect
A 12-week fetal sonogram in a 22-year-old gravida 1 para 0 pregnant woman was suggestive of multiple anomalies. She had previously been known to have a congenital uterine anomaly with both a vaginal septum and uterine septum that were removed 3 years before the current pregnancy. She had 2 cervices with 1 uterine cavity and normal bilateral fallopian tubes at the time of this spontaneous pregnancy. Her initial course was uncomplicated, but fetal ultrasonography at 12 weeks’ gestation showed a large anterior wall defect spanning both the chest and abdomen, with concern for possible ectopia cordis. The fetus was noted to have restricted movement within a subjectively small amniotic sac. Cell-free fetal DNA screening was low risk and amniocentesis was not performed. She was referred to the Maternal Fetal Care Center (MFCC) at 17 weeks’ gestation for further evaluation and counseling.
Imaging at the MFCC at 17 weeks and 3 days of gestation included fetal ultrasonography, magnetic resonance imaging (MRI), and fetal echocardiography. Imaging findings included the following: a small portion of the cardiac apex was outside the chest, a small anterior diaphragmatic hernia, a large abdominal wall defect with liver and bowel herniation, and significant thoracic kyphosis (Fig 1). In addition, a complete amnion-chorion separation was noted (Fig 2, ultrasound scan; Fig 3, MRI). Fetal echocardiography showed a ventricular septal defect (VSD) with a mildly hypoplastic pulmonary valve and partial ectopia cordis with a small sternal defect with left cardiac ventricle herniation.
Figure 1.
Prenatal T2 magnetic resonance imaging at 17 weeks’ gestation demonstrating herniated liver (solid green arrow) and small herniation of the apex of the heart (dashed green arrow) through a diaphragmatic defect. Evisceration of the small bowel (solid yellow arrow) and colon (dashed yellow arrow) are also visualized. Demonstration of kyphoscoliosis (red arrow).
Figure 2.
Ultrasound image demonstrating
Can Dynamic Magnetic Resonance Images Improve Prenatal Diagnosis of Robin Sequence
Background: Robin sequence (RS) is a triad of micrognathia, glossoptosis, and airway obstruction. Prenatal diagnosis of RS improves delivery planning and postnatal care, but the process for prenatal diagnosis has not been refined. The purpose of this study was to determine if dynamic cine magnetic resonance imaging (MRI) can improve the reliability of prenatal diagnosis for RS compared to current static imaging techniques. Materials and Methods: This is a retrospective cross-sectional study including fetuses with prenatal MRIs obtained in a single center from January 2014 to November 2019. Fetuses were included if they: 1) had a prenatal MRI with cine dynamic sequences of adequate quality, 2) were live born, and 3) had postnatal craniofacial evaluation to confirm RS. Patients without postnatal confirmation of their prenatal findings were excluded. The primary predictor variable was imaging type (cine or static MRI). Outcome variables were tongue and airway measurements: 1) tongue height, 2) length and width, 3) tongue shape index, 4) observation of tongue touching the posterior pharyngeal wall, and 5) measurement of oropharyngeal space. All measurements were made independently on the cine images and on static MRI sequences for the same cohort of subjects by a pediatric radiologist. Data were analyzed using paired samples t tests and Fisher exact tests, and significance was set as P < .05. Results: A total of 11 patients with RS were included in the study. The smallest airway space consistently demonstrated complete collapse on the cine series compared to partial collapse on static images (0 mm vs 1.7 ± 1.4 mm, P = .002). No other imaging variable was statistically significantly different between techniques. Conclusions: Cine imaging sequences on prenatal MRI were superior to static images in discerning complete collapse of the smallest airway space, an important marker of RS. This suggests a possible benefit to adding dynamic MRI evaluation for prenatal diagnosis of this condition
Outcome of Fetuses With Cerebral Ventriculomegaly and Septum Pellucidum Leaflet Abnormalities
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