Convolutional Neural Network for Automated FLAIR Lesion Segmentation on Clinical Brain MR Imaging.

BACKGROUND AND PURPOSE: Most brain lesions are characterized by hyperintense signal on FLAIR. We sought to develop an automated deep learning-based method for segmentation of abnormalities on FLAIR and volumetric quantification on clinical brain MRIs across many pathologic entities and scanning parameters. We evaluated the performance of the algorithm compared with manual segmentation and existing automated methods. MATERIALS AND METHODS: We adapted a U-Net convolutional neural network architecture for brain MRIs using 3D volumes. This network was retrospectively trained on 295 brain MRIs to perform automated FLAIR lesion segmentation. Performance was evaluated on 92 validation cases using Dice scores and voxelwise sensitivity and specificity, compared with radiologists' manual segmentations. The algorithm was also evaluated on measuring total lesion volume. RESULTS: Our model demonstrated accurate FLAIR lesion segmentation performance (median Dice score, 0.79) on the validation dataset across a large range of lesion characteristics. Across 19 neurologic diseases, performance was significantly higher than existing methods (Dice, 0.56 and 0.41) and approached human performance (Dice, 0.81). There was a strong correlation between the predictions of lesion volume of the algorithm compared with true lesion volume (ρ = 0.99). Lesion segmentations were accurate across a large range of image-acquisition parameters on >30 different MR imaging scanners. CONCLUSIONS: A 3D convolutional neural network adapted from a U-Net architecture can achieve high automated FLAIR segmentation performance on clinical brain MR imaging across a variety of underlying pathologies and image acquisition parameters. The method provides accurate volumetric lesion data that can be incorporated into assessments of disease burden or into radiologic reports.

Mapping of pain circuitry in early post-natal development using manganese-enhanced MRI in rats.

Premature or ill full-term infants are subject to a number of noxious procedures as part of their necessary medical care. Although we know that human infants show neural changes in response to such procedures, we know little of the sensory or affective brain circuitry activated by pain. In rodent models, the focus has been on spinal cord and, more recently, midbrain and medulla. The present study assesses activation of brain circuits using manganese-enhanced magnetic resonance imaging (MEMRI). Uptake of manganese, a paramagnetic contrast agent that is transported across active synapses and along axons, was measured in response to a hindpaw injection of dilute formalin in 12-day-old rat pups, the age at which rats begin to show aversion learning and which is roughly the equivalent of full-term human infants. Formalin induced the oft-reported biphasic response at this age and induced a conditioned aversion to cues associated with its injection, thus demonstrating the aversiveness of the stimulation. Morphometric analyses, structural equation modeling and co-expression analysis showed that limbic and sensory paths were activated, the most prominent of which were the prefrontal and anterior cingulate cortices, nucleus accumbens, amygdala, hypothalamus, several brainstem structures, and the cerebellum. Therefore, both sensory and affective circuits, which are activated by pain in the adult, can also be activated by noxious stimulation in 12-day-old rat pups.

Assessment of MRI-Based Automated Fetal Cerebral Cortical Folding Measures in Prediction of Gestational Age in the Third Trimester.

BACKGROUND AND PURPOSE: Traditional methods of dating a pregnancy based on history or sonographic assessment have a large variation in the third trimester. We aimed to assess the ability of various quantitative measures of brain cortical folding on MR imaging in determining fetal gestational age in the third trimester. MATERIALS AND METHODS: We evaluated 8 different quantitative cortical folding measures to predict gestational age in 33 healthy fetuses by using T2-weighted fetal MR imaging. We compared the accuracy of the prediction of gestational age by these cortical folding measures with the accuracy of prediction by brain volume measurement and by a previously reported semiquantitative visual scale of brain maturity. Regression models were constructed, and measurement biases and variances were determined via a cross-validation procedure. RESULTS: The cortical folding measures are accurate in the estimation and prediction of gestational age (mean of the absolute error, 0.43 ± 0.45 weeks) and perform better than (P = .024) brain volume (mean of the absolute error, 0.72 ± 0.61 weeks) or sonography measures (SDs approximately 1.5 weeks, as reported in literature). Prediction accuracy is comparable with that of the semiquantitative visual assessment score (mean, 0.57 ± 0.41 weeks). CONCLUSIONS: Quantitative cortical folding measures such as global average curvedness can be an accurate and reliable estimator of gestational age and brain maturity for healthy fetuses in the third trimester and have the potential to be an indicator of brain-growth delays for at-risk fetuses and preterm neonates.

Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain.

One of the most challenging problems in modern neuroimaging is detailed characterization of neurodegeneration. Quantifying spatial and longitudinal atrophy patterns is an important component of this process. These spatiotemporal signals will aid in discriminating between related diseases, such as frontotemporal dementia (FTD) and Alzheimer's disease (AD), which manifest themselves in the same at-risk population. Here, we develop a novel symmetric image normalization method (SyN) for maximizing the cross-correlation within the space of diffeomorphic maps and provide the Euler-Lagrange equations necessary for this optimization. We then turn to a careful evaluation of our method. Our evaluation uses gold standard, human cortical segmentation to contrast SyN's performance with a related elastic method and with the standard ITK implementation of Thirion's Demons algorithm. The new method compares favorably with both approaches, in particular when the distance between the template brain and the target brain is large. We then report the correlation of volumes gained by algorithmic cortical labelings of FTD and control subjects with those gained by the manual rater. This comparison shows that, of the three methods tested, SyN's volume measurements are the most strongly correlated with volume measurements gained by expert labeling. This study indicates that SyN, with cross-correlation, is a reliable method for normalizing and making anatomical measurements in volumetric MRI of patients and at-risk elderly individuals.

Multivariate normalization with symmetric diffeomorphisms for multivariate studies.

Current clinical and research neuroimaging protocols acquire images using multiple modalities, for instance, T1, T2, diffusion tensor and cerebral blood flow magnetic resonance images (MRI). These multivariate datasets provide unique and often complementary anatomical and physiological information about the subject of interest. We present a method that uses fused multiple modality (scalar and tensor) datasets to perform intersubject spatial normalization. Our multivariate approach has the potential to eliminate inconsistencies that occur when normalization is performed on each modality separately. Furthermore, the multivariate approach uses a much richer anatomical and physiological image signature to infer image correspondences and perform multivariate statistical tests. In this initial study, we develop the theory for Multivariate Symmetric Normalization (MVSyN), establish its feasibility and discuss preliminary results on a multivariate statistical study of 22q deletion syndrome.

An automated approach to connectivity-based partitioning of brain structures.

We present an automated approach to the problem of connectivity-based partitioning of brain structures using diffusion imaging. White-matter fibres connect different areas of the brain, allowing them to interact with each other. Diffusion-tensor MRI measures the orientation of white-matter fibres in vivo, allowing us to perform connectivity-based partitioning non-invasively. Our new approach leverages atlas-based segmentation to automate anatomical labeling of the cortex. White-matter connectivities are inferred using a probabilistic tractography algorithm that models crossing pathways explicitly. The method is demonstrated with the partitioning of the corpus callosum of eight healthy subjects.

The use of inert carriers in regulatory biodegradation tests of low density poorly water-soluble substances.

Many poorly water-soluble compounds fail regulatory ready biodegradation tests as the method of test material preparation limits the bioavailability of the chemical. The recognised method for delivery of poorly soluble materials into biodegradability tests consists of coating test material inside the test vessel or onto inert substrates (i.e., glass cover slide, boiling beads, filter paper, or Teflon stir bar) that are placed inside the vessels. Volatile solvents are often used to augment this process. Although these substrates work fairly well for delivering many poorly soluble materials into biodegradability tests, they have not been effective in keeping low density, poorly water-soluble substances in the test medium. Soon after medium is added to the test vessels, these chemicals break loose from the substrates and float on the surface where they have limited contact with micro-organisms in the test medium. Hence, there is a reduced potential for measuring substantial biodegradability in the test. This paper describes the work undertaken to establish a standard method of adding low density, poorly water-soluble substances into test vessels of biodegradability studies to ensure these materials remain in contact with micro-organisms in the test medium. The substances are prepared for testing by adsorption onto silica gel followed by dispersion into the culture medium. This method of delivery may provide greater intra- and inter-laboratory consistency in biodegradability test results for low density, poorly water-soluble substances and it may more closely mimic the probable transport and fate of these substances in the environment.

Spatial transformations of diffusion tensor magnetic resonance images.

We address the problem of applying spatial transformations (or "image warps") to diffusion tensor magnetic resonance images. The orientational information that these images contain must be handled appropriately when they are transformed spatially during image registration. We present solutions for global transformations of three-dimensional images up to 12-parameter affine complexity and indicate how our methods can be extended for higher order transformations. Several approaches are presented and tested using synthetic data. One method, the preservation of principal direction algorithm, which takes into account shearing, stretching and rigid rotation, is shown to be the most effective. Additional registration experiments are performed on human brain data obtained from a single subject, whose head was imaged in three different orientations within the scanner. All of our methods improve the consistency between registered and target images over naïve warping algorithms.

Elastically deforming 3D atlas to match anatomical brain images.

To evaluate our system for elastically deforming a three-dimensional atlas to match anatomical brain images, six deformed versions of an atlas were generated. The deformed atlases were created by elastically mapping an anatomical brain atlas onto different MR brain image volumes. The mapping matches the edges of the ventricles and the surface of the brain; the resultant deformations are propagated through the atlas volume, deforming the remainder of the structures in the process. The atlas was then elastically matched to its deformed versions. The accuracy of the resultant matches was evaluated by determining the correspondence of 32 cortical and subcortical structures. The system on average matched the centroid of a structure to within 1 mm of its true position and fit a structure to within 11% of its true volume. The overlap between the matched and true structures, defined by the ratio between the volume of their intersection and the volume of their union, averaged 66%. When the gray-white interface was included for matching, the mean overlap improved to 78%; each structure was matched to within 0.6 mm of its true position and fit to within 6% of its true volume. Preliminary studies were also made to determine the effect of the compliance of the atlas on the resultant match.