7 research outputs found
Learning to segment fetal brain tissue from noisy annotations
Automatic fetal brain tissue segmentation can enhance the quantitative
assessment of brain development at this critical stage. Deep learning methods
represent the state of the art in medical image segmentation and have also
achieved impressive results in brain segmentation. However, effective training
of a deep learning model to perform this task requires a large number of
training images to represent the rapid development of the transient fetal brain
structures. On the other hand, manual multi-label segmentation of a large
number of 3D images is prohibitive. To address this challenge, we segmented 272
training images, covering 19-39 gestational weeks, using an automatic
multi-atlas segmentation strategy based on deformable registration and
probabilistic atlas fusion, and manually corrected large errors in those
segmentations. Since this process generated a large training dataset with noisy
segmentations, we developed a novel label smoothing procedure and a loss
function to train a deep learning model with smoothed noisy segmentations. Our
proposed methods properly account for the uncertainty in tissue boundaries. We
evaluated our method on 23 manually-segmented test images of a separate set of
fetuses. Results show that our method achieves an average Dice similarity
coefficient of 0.893 and 0.916 for the transient structures of younger and
older fetuses, respectively. Our method generated results that were
significantly more accurate than several state-of-the-art methods including
nnU-Net that achieved the closest results to our method. Our trained model can
serve as a valuable tool to enhance the accuracy and reproducibility of fetal
brain analysis in MRI
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
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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
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
Image_1_Abnormal prenatal brain development in Chiari II malformation.jpg
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.</p
Image_2_Abnormal prenatal brain development in Chiari II malformation.jpg
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.</p