51 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
Diffusion tensor driven image registration: a deep learning approach
Tracking microsctructural changes in the developing brain relies on accurate
inter-subject image registration. However, most methods rely on either
structural or diffusion data to learn the spatial correspondences between two
or more images, without taking into account the complementary information
provided by using both. Here we propose a deep learning registration framework
which combines the structural information provided by T2-weighted (T2w) images
with the rich microstructural information offered by diffusion tensor imaging
(DTI) scans. We perform a leave-one-out cross-validation study where we compare
the performance of our multi-modality registration model with a baseline model
trained on structural data only, in terms of Dice scores and differences in
fractional anisotropy (FA) maps. Our results show that in terms of average Dice
scores our model performs better in subcortical regions when compared to using
structural data only. Moreover, average sum-of-squared differences between
warped and fixed FA maps show that our proposed model performs better at
aligning the diffusion data
Efficient multi-class fetal brain segmentation in high resolution MRI reconstructions with noisy labels
Segmentation of the developing fetal brain is an important step in
quantitative analyses. However, manual segmentation is a very time-consuming
task which is prone to error and must be completed by highly specialized
indi-viduals. Super-resolution reconstruction of fetal MRI has become standard
for processing such data as it improves image quality and resolution. However,
dif-ferent pipelines result in slightly different outputs, further complicating
the gen-eralization of segmentation methods aiming to segment super-resolution
data. Therefore, we propose using transfer learning with noisy multi-class
labels to automatically segment high resolution fetal brain MRIs using a single
set of seg-mentations created with one reconstruction method and tested for
generalizability across other reconstruction methods. Our results show that the
network can auto-matically segment fetal brain reconstructions into 7 different
tissue types, regard-less of reconstruction method used. Transfer learning
offers some advantages when compared to training without pre-initialized
weights, but the network trained on clean labels had more accurate
segmentations overall. No additional manual segmentations were required.
Therefore, the proposed network has the potential to eliminate the need for
manual segmentations needed in quantitative analyses of the fetal brain
independent of reconstruction method used, offering an unbiased way to quantify
normal and pathological neurodevelopment.Comment: Accepted for publication at PIPPI MICCAI 202
PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRI
In this paper we present a novel method for the correction of motion
artifacts that are present in fetal Magnetic Resonance Imaging (MRI) scans of
the whole uterus. Contrary to current slice-to-volume registration (SVR)
methods, requiring an inflexible anatomical enclosure of a single investigated
organ, the proposed patch-to-volume reconstruction (PVR) approach is able to
reconstruct a large field of view of non-rigidly deforming structures. It
relaxes rigid motion assumptions by introducing a specific amount of redundant
information that is exploited with parallelized patch-wise optimization,
super-resolution, and automatic outlier rejection. We further describe and
provide an efficient parallel implementation of PVR allowing its execution
within reasonable time on commercially available graphics processing units
(GPU), enabling its use in the clinical practice. We evaluate PVR's
computational overhead compared to standard methods and observe improved
reconstruction accuracy in presence of affine motion artifacts of approximately
30% compared to conventional SVR in synthetic experiments. Furthermore, we have
evaluated our method qualitatively and quantitatively on real fetal MRI data
subject to maternal breathing and sudden fetal movements. We evaluate
peak-signal-to-noise ratio (PSNR), structural similarity index (SSIM), and
cross correlation (CC) with respect to the originally acquired data and provide
a method for visual inspection of reconstruction uncertainty. With these
experiments we demonstrate successful application of PVR motion compensation to
the whole uterus, the human fetus, and the human placenta.Comment: 10 pages, 13 figures, submitted to IEEE Transactions on Medical
Imaging. v2: wadded funders acknowledgements to preprin
Impaired development of the cerebral cortex in infants with congenital heart disease is correlated to reduced cerebral oxygen delivery
Neurodevelopmental impairment is the most common comorbidity associated with complex congenital heart disease (CHD), while the underlying biological mechanism remains unclear. We hypothesised that impaired cerebral oxygen delivery in infants with CHD is a cause of impaired cortical development, and predicted that cardiac lesions most associated with reduced cerebral oxygen delivery would demonstrate the greatest impairment of cortical development. We compared 30 newborns with complex CHD prior to surgery and 30 age-matched healthy controls using brain MRI. The cortex was assessed using high resolution, motion-corrected T2-weighted images in natural sleep, analysed using an automated pipeline. Cerebral oxygen delivery was calculated using phase contrast angiography and pre-ductal pulse oximetry, while regional cerebral oxygen saturation was estimated using near-infrared spectroscopy. We found that impaired cortical grey matter volume and gyrification index in newborns with complex CHD was linearly related to reduced cerebral oxygen delivery, and that cardiac lesions associated with the lowest cerebral oxygen delivery were associated with the greatest impairment of cortical development. These findings suggest that strategies to improve cerebral oxygen delivery may help reduce brain dysmaturation in newborns with CHD, and may be most relevant for children with CHD whose cardiac defects remain unrepaired for prolonged periods after birth
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