4 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
Novel Image Processing Methods for Improved Fetal Brain MRI
Fetal magnetic resonance imaging (MRI) has been increasingly used as a powerful complement
imaging modality to ultrasound imaging (US) for the clinical evaluation of prenatal
abnormalities. Specifically, clinical application of fetal MRI has been significantly improved in
the nineties by hardware and software advances with the development of ultrafast multi-slice
T2-weighted (T2w) acquisition sequences able to freeze the unpredictable fetal motion and
provide excellent soft-tissue contrast. Fetal motion is indeed the major challenge in fetal
MRI and slice acquisition time should be kept as short as possible. As a result, typical fetal
MRI examination involves the acquisition of a set of orthogonally planned scans of thick
two-dimensional slices, largely free of intra-slice motion artifacts. The poor resolution in
the slice-select dimension as well as possible motion occurring between slices limits further
quantitative data analysis, which is the key for a better understanding of the developing
brain but also the key for the determination of operator-independent biomarkers that might
significantly facilitate fetal diagnosis and prognosis.
To this end, several research groups have developed in the past ten years advanced image
processing methods, often denoted by motion-robust super-resolution (SR) techniques, to
reconstruct from a set of clinical low-resolution (LR) scans, a high-resolution (HR) motion-free
volume. SR problem is usually modeled as a linear inverse problem describing the imaging
degradation due to acquisition and fetal motion. Typically, such approaches consist in iterating
between slice motion estimation that estimates the motion parameters and SR that recovers
the HR image given the estimated degradation model. This thesis focuses on the development
of novel advanced image processing methods, which have enabled the design of a completely
automated reconstruction pipeline for fetal MRI. The proposed techniques help in improving
state-of-the-art fetal MRI reconstruction in terms of efficiency, robustness and minimized
user-interactions, with the ultimate goal of being translated to the clinical environment.
The first part focuses on the development of a more efficient Total Variation (TV)-regularized
optimization algorithm for the SR problem. The algorithm uses recent advances in convex optimization
with a novel adaptive regularization strategy to offer simultaneously fast, accurate
and robust solutions to the fetal image recovery problem. Extensive validations on both
simulated fetal and real clinical data show the proposed algorithm is highly robust in front of
motion artifacts and that it offers the best trade-off between speed and accuracy for fetal MRI
recovery as in comparison with state-of-the art methods.
The second part focuses on the development of a novel automatic brain localization and
extraction approach based on template-to-slice block matching and deformable slice-totemplate
registration. Asmost fetal brain MRI reconstruction algorithms rely only on brain
tissue-relevant voxels of low-resolution (LR) images to enhance the quality of inter-slice motion
correction and image reconstruction, the fetal brain needs to be localized and extracted as
a first step. These tasks generally necessitate user interaction, manually or semi-automatically
done. Our methods have enabled the design of completely automated reconstruction pipeline
that involves intensity normalization, inter-slice motion estimation, and super-resolution.
Quantitative evaluation on clinical MRI scans shows that our approach produces brain masks
that are very close to manually drawn brain masks, and ratings performed by two expert
observers show that the proposed pipeline achieves similar reconstruction quality to reference
reconstruction based on manual slice-by-slice brain extraction without any further effort.
The third part investigates the possibility of automatic cortical folding quantification, one of
the best biomarkers of brain maturation, by combining our automatic reconstruction pipeline
with a state-of-the-art fetal brain tissue segmentation method and existing automated tools
provided for adult brain’s cortical folding quantification. Results indicate that our reconstruction
pipeline can provide HR MR images with sufficient quality that enable the use of surface
tessellation and active surface algorithms similar to those developed for adults to extract
meaningful information about fetal brain maturation.
Finally, the last part presents new methodological improvements of the reconstruction
pipeline aiming at improving the quality of the image for quantitative data analysis, whose
accuracy is highly dependent on the quality and resolution of the reconstructed image. In
particular, it presents a more consistent and global magnetic bias field correction method
which takes advantage of the super-resolution framework to provide a final reconstructed
image quasi free of the smooth bias field. Then, it presents a new TV SR algorithm that uses
the Huber norm in the data fidelity term to be more robust to non-Gaussian outliers. It
also presents the design of a novel joint reconstruction-segmentation framework and the
development of a novel TV SR algorithm driven by segmentation to produce images with
enhanced edge information that could ultimately improve their segmentation. Finally, it
preliminary investigates the capability of increasing the resolution in the in-plane dimensions
using SR to ultimately reduce the partial volume effect
Automatic Brain Extraction in Fetal MRI using Multi-Atlas-based Segmentation
In fetal brain MRI, most of the high-resolution reconstruction algorithms rely on brain segmentation as a preprocessing step. Manual brain segmentation is however highly time-consuming and therefore not a realistic solution. In this work, we assess on a large dataset the performance of Multiple Atlas Fusion (MAF) strategies to automatically address this problem. Firstly, we show that MAF significantly increase the accuracy of brain segmentation as regards single-atlas strategy. Secondly, we show that MAF compares favorably with the most recent approach (Dice above 0.90). Finally, we show that MAF could in turn provide an enhancement in terms of reconstruction quality