Semi-automatic segmentation of the fetal brain from magnetic resonance imaging

Background: Volumetric measurements of fetal brain maturation in the third trimester of pregnancy are key predictors of developmental outcomes. Improved understanding of fetal brain development trajectories may aid in identifying and clinically managing at-risk fetuses. Currently, fetal brain structures in magnetic resonance images (MRI) are often manually segmented, which requires both time and expertise. To facilitate the targeting and measurement of brain structures in the fetus, we compared the results of five segmentation methods applied to fetal brain MRI data to gold-standard manual tracings. Methods: Adult women with singleton pregnancies (n = 21), of whom five were scanned twice, approximately 3 weeks apart, were recruited [26 total datasets, median gestational age (GA) = 34.8, IQR = 30.9–36.6]. T2-weighted single-shot fast spin echo images of the fetal brain were acquired on 1.5T and 3T MRI scanners. Images were first combined into a single 3D anatomical volume. Next, a trained tracer manually segmented the thalamus, cerebellum, and total cerebral volumes. The manual segmentations were compared with five automatic methods of segmentation available within Advanced Normalization Tools (ANTs) and FMRIB’s Linear Image Registration Tool (FLIRT) toolboxes. The manual and automatic labels were compared using Dice similarity coefficients (DSCs). The DSC values were compared using Friedman’s test for repeated measures. Results: Comparing cerebellum and thalamus masks against the manually segmented masks, the median DSC values for ANTs and FLIRT were 0.72 [interquartile range (IQR) = 0.6–0.8] and 0.54 (IQR = 0.4–0.6), respectively. A Friedman’s test indicated that the ANTs registration methods, primarily nonlinear methods, performed better than FLIRT (p \u3c 0.001). Conclusion: Deformable registration methods provided the most accurate results relative to manual segmentation. Overall, this semi-automatic subcortical segmentation method provides reliable performance to segment subcortical volumes in fetal MR images. This method reduces the costs of manual segmentation, facilitating the measurement of typical and atypical fetal brain development

Free-Space Features: Global Localization in 2D Laser SLAM Using Distance Function Maps

In many applications, maintaining a consistent map of the environment is key to enabling robotic platforms to perform higher-level decision making. Detection of already visited locations is one of the primary ways in which map consistency is maintained, especially in situations where external positioning systems are unavailable or unreliable. Mapping in 2D is an important field in robotics, largely due to the fact that man-made environments such as warehouses and homes, where robots are expected to play an increasing role, can often be approximated as planar. Place recognition in this context remains challenging: 2D lidar scans contain scant information with which to characterize, and therefore recognize, a location. This paper introduces a novel approach aimed at addressing this problem. At its core, the system relies on the use of the distance function for representation of geometry. This representation allows extraction of features which describe the geometry of both surfaces and free-space in the environment. We propose a feature for this purpose. Through evaluations on public datasets, we demonstrate the utility of free-space in the description of places, and show an increase in localization performance over a state-of-the-art descriptor extracted from surface geometry

Word ordering and document adjacency for large loop closure detection in 2D laser maps

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksWe address in this paper the problem of loop closure detection for laser-based simultaneous localization and mapping (SLAM) of very large areas. Consistent with the state of the art, the map is encoded as a graph of poses, and to cope with very large mapping capabilities, loop closures are asserted by comparing the features extracted from a query laser scan against a previously acquired corpus of scan features using a bag-ofwords (BoW) scheme. Two contributions are here presented. First, to benefit from the graph topology, feature frequency scores in the BoW are computed not only for each individual scan but also from neighboring scans in the SLAM graph. This has the effect of enforcing neighbor relational information during document matching. Secondly, a weak geometric check that takes into account feature ordering and occlusions is introduced that substantially improves loop closure detection performance. The two contributions are evaluated both separately and jointly on four common SLAM datasets, and are shown to improve the state-of-the-art performance both in terms of precision and recall in most of the cases. Moreover, our current implementation is designed to work at nearly frame rate, allowing loop closure query resolution at nearly 22 Hz for the best case scenario and 2 Hz for the worst case scenario.Peer ReviewedPostprint (author's final draft

Effect of scan time on resting state parameters

In the past decade the interest in studying the spontaneous low-frequency fluctuations (LFF) in a resting-state brain has steadily grown. By measuring LFF (\u3c 0.08 Hz) in blood-oxygen-level-dependent (BOLD) signals, resting-state functional magnetic resonance imaging (rs-fMRI) has proven to be a powerful tool in exploring brain network connectivity and functionality. Rs-fMRI data can be used to organize the brain into resting state networks (RSNs). In this thesis, rs-fMRI data are used to determine the minimum data acquisition time necessary to detect local intrinsic brain activity as a function of both the amplitude of low frequency fluctuations (ALFF) and the fractional amplitude of low frequency fluctuations (fALFF) in BOLD signals in healthy subjects. The data are obtained from 22 healthy subjects to use as a baseline for future rs-fMRI analysis. Voxel-wise analysis is performed on the whole brain, gray matter volume, and two previously established RSNs: the default mode network (DMN) and the visual system network, for all the subjects in this study. Pearson’s correlation coefficients (r-values) are calculated from each subject. The entire time series for one subject is divided into 31 subsections and the r-values are calculated between each consecutive subsection in a subject. In total, there are 30 r- values. To better understand what the results mean across subjects and within subjects Fisher transformations are applied to the 30 calculated r-values for each subject to get a normal z-distribution. The mean across 22 subjects’ z-values is calculated for group analysis. In the end, there are 30 mean values. Finally, an exponential curve fit model is calculated across the 22 subjects using the calculated mean values, and an asymptotic growth model is used to detect the minimum data acquisition time required to obtain both ALFF and fALFF of the BOLD signals at rest. The results show that the minimum time required to detect an ALFF and fALFF of the BOLD signals at rest is 12 and 13.33 minutes respectively. Future studies can focus on determining the minimum scanner time using similar analysis for different physiological states of the brain

Implementation of anatomical navigators for real time motion correction in diffusion tensor imaging

Includes bibliographical references.Prospective motion correction methods using an optical system, diffusion-weighted prospective acquisition correction, or a free induction decay navigator have recently been applied to correct for motion in diffusion tensor imaging. These methods have some limitations and drawbacks. This article describes a novel technique using a three-dimensional-echo planar imaging navigator, of which the contrast is independent of the b-value, to perform prospective motion correction in diffusion weighted images, without having to reacquire volumes during which motion occurred, unless motion exceeded some preset thresholds. Water phantom and human brain data were acquired using the standard and navigated diffusion sequences, and the mean and whole brain histogram of the fractional anisotropy and mean diffusivity were analyzed

Extent of resection of peritumoral diffusion tensor imaging-detected abnormality as a predictor of survival in adult glioblastoma patients

OBJECTIVE Diffusion tensor imaging (DTI) has been shown to detect tumor invasion in glioblastoma patients and has been applied in surgical planning. However, the clinical value of the extent of resection based on DTI is unclear. Therefore, the correlation between the extent of resection of DTI abnormalities and patients' outcome was retrospectively reviewed.METHODS A review was conducted of 31 patients with newly diagnosed supratentorial glioblastoma who underwent standard 5-aminolevulinic-acid aided surgery with the aim of maximal resection of the enhancing tumor component. All patients underwent presurgical MRI, including volumetric postcontrast T1-weighted imaging, DTI, and FLAIR. Postsurgical anatomical MR images were obtained within 72 hours of resection. The diffusion tensor was split into an isotropic (p) and anisotropic (q) component. The extent of resection was measured for the abnormal area on the p, q, FLAIR, and postcontrast T1-weighted images. Data were analyzed in relation to patients' outcome using univariate and multivariate Cox regression models controlling for possible confounding factors including age, O-6-methylguanine-DNA-methyltransferase methylation status, and isocitrate dehydrogenase-1 mutation.RESULTS Complete resection of the enhanced tumor shown on the postcontrast II-weighted images was achieved in 24 of 31 patients (77%). The mean extent of resection of the abnormal p, q, and FLAIR areas was 57%, 83%, and 59%, respectively. Increased resection of the abnormal p and q areas correlated positively with progression-free survival (p = 0.009 and p = 0.006, respectively). Additionally, a larger, residual, abnormal q volume predicted significantly shorter time to progression (p = 0.008). More extensive resection of the abnormal q and contrast-enhanced area improved overall survival (p = 0.041 and 0.050, respectively).CONCLUSIONS Longer progression-free survival and overall survival were seen in glioblastoma patients in whom more DTI-documented abnormality was resected, which was previously shown to represent infiltrative tumor. This highlights the potential usefulness and the importance of an extended resection based on DTI-derived maps.</p