Automated fetal brain segmentation from 2D MRI slices for motion correction

Abstract

Motion correction is a key element for imaging the fetal brain in-utero using Magnetic Resonance Imaging (MRI). Maternal breathing can introduce motion, but a larger effect is frequently due to fetal movement within the womb. Consequently, imaging is frequently performed slice-by-slice using single shot techniques, which are then combined into volumetric images using slice-tovolume reconstruction methods (SVR). For successful SVR, a key preprocessing step is to isolate fetal brain tissues from maternal anatomy before correcting for the motion of the fetal head. This has hitherto been a manual or semi-automatic procedure. We propose an automatic method to localize and segment the brain of the fetus when the image data is acquired as stacks of 2D slices with anatomy misaligned due to fetal motion. We combine this segmentation process with a robust motion correction method, enabling the segmentation to be refined as the reconstruction proceeds. The fetal brain localization process uses Maximally Stable Extremal Regions (MSER), which are classified using a Bag-of-Words model with Scale-Invariant Feature Transform (SIFT) features. The segmentation process is a patch-based propagation of the MSER regions selected during detection, combined with a Conditional Random Field (CRF). The gestational age (GA) is used to incorporate prior knowledge about the size and volume of the fetal brain into the detection and segmentation process. The method was tested in a ten-fold cross-validation experiment on 66 datasets of healthy fetuses whose GA ranged from 22 to 39 weeks. In 85% of the tested cases, our proposed method produced a motion corrected volume of a relevant quality for clinical diagnosis, thus removing the need for manually delineating the contours of the brain before motion correction. Our method automatically generated as a side-product a segmentation of the reconstructed fetal brain with a mean Dice score of 93%, which can be used for further processing.

Flexible Reconstruction and Correction of Unpredictable Motion from Stacks of 2D Images

“…However, we can compare the reconstruction quality with a well researched organ: the fetal brain. Our hypothesis is that our method provides similar reconstruction and motion correction quality for the brain as it would be the case if a tight mask [7] for a region of interest would have been used for SVR. We expect the quality of our results to be close to the results from the state-of-the-art SVR approach [5] for rigid regions.…”

“…This can be useful to apply the classic SVR reconstruction approach at a higher level of detail only in these regions. As shown by [7], tight region of interest masks can lead to a higher reconstruction quality for rigid regions like the fetal brain. In practice we can identify such rigid regions by blurring P using a 3D Gaussian filter with σ related to the size of the desired regions followed by blob detection.…”

“…Often six to twelve stacks need to be acquired to sufficiently oversample a 3D volume. Segmentation and localization of selected organs can be automated, however, the available approaches provide either a very rough segmentation of the central slices of a stack [7] or require less motion corrupted stacks [4]. Furthermore, they are only applicable to a specificity trained region, e.g., the fetal brain.…”

PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRI

“…SVR methods have been successfully employed to address these problems in fetal MR and are typically applied to small regions and organs with rigid body characteristics that are identified by manual annotations [16]–[19] or less precise, automated segmentations [20]–[22]. Such approaches are prohibitive to whole body and uterus reconstruction because of the assumption of rigid motion in the 2D to 3D registration step of SVR.…”

Automatic Brain Localization in Fetal MRI Using Superpixel Graphs

“…Fetal brain localization is important for assessing the fetal brain development and maturation. It is also the primary step for most of the current automatic motion correction techniques for fetal MRI [11]. Recently, fetal brain detection has been used as a landmark to extract the other fetal organs [13].…”