Segmentation and quantitative evaluation of brain MRI data with a multi-phase three-dimensional implicit deformable model

Segmentation of three-dimensional anatomical brain images into tissue classes has applications in both clinical and research settings. This paper presents the implementation and quantitative evaluation of a four-phase three-dimensional active contour implemented with a level set framework for automated segmentation of brain MRIs. The segmentation algorithm performs an optimal partitioning of three-dimensional data based on homogeneity measures that naturally evolves to the extraction of different tissue types in the brain. Random seed initialization was used to speed up numerical computation and avoid the need for a priori information. This random initialization ensures robustness of the method to variation of user expertise, biased a priori information and errors in input information that could be influenced by variations in image quality. Experimentation on three MRI brain data sets showed that an optimal partitioning successfully labeled regions that accurately identified white matter, gray matter and cerebrospinal fluid in the ventricles. Quantitative evaluation of the segmentation was performed with comparison to manually labeled data and computed false positive and false negative assignments of voxels for the three organs. We report high accuracy for the two comparison cases. These results demonstrate the efficiency and flexibility of this segmentation framework to perform the challenging task of automatically extracting brain tissue volume contours

Brain MRI Segmentation with Multiphase Minimal Partitioning: A Comparative Study

This paper presents the implementation and quantitative evaluation of a multiphase three-dimensional deformable model in a level set framework for automated segmentation of brain MRIs. The segmentation algorithm performs an optimal partitioning of three-dimensional data based on homogeneity measures that naturally evolves to the extraction of different tissue types in the brain. Random seed initialization was used to minimize the sensitivity of the method to initial conditions while avoiding the need for a priori information. This random initialization ensures robustness of the method with respect to the initialization and the minimization set up. Postprocessing corrections with morphological operators were applied to refine the details of the global segmentation method. A clinical study was performed on a database of 10 adult brain MRI volumes to compare the level set segmentation to three other methods: “idealized” intensity thresholding, fuzzy connectedness, and an expectation maximization classification using hidden Markov random fields. Quantitative evaluation of segmentation accuracy was performed with comparison to manual segmentation computing true positive and false positive volume fractions. A statistical comparison of the segmentation methods was performed through a Wilcoxon analysis of these error rates and results showed very high quality and stability of the multiphase three-dimensional level set method

Multi-Phase Three-Dimensional Level Set Segmentation of Brain MRI

Segmentation of cortical structures on clinical brain MRI data is applied for clinical study of depression on elderly subjects. Segmentation of clinical MRI data is more challenging than with simulated data due to intensity overlap among cortical structures and non-homogeneity within each tissue

Mindboggle: Automated brain labeling with multiple atlases

BACKGROUND: To make inferences about brain structures or activity across multiple individuals, one first needs to determine the structural correspondences across their image data. We have recently developed Mindboggle as a fully automated, feature-matching approach to assign anatomical labels to cortical structures and activity in human brain MRI data. Label assignment is based on structural correspondences between labeled atlases and unlabeled image data, where an atlas consists of a set of labels manually assigned to a single brain image. In the present work, we study the influence of using variable numbers of individual atlases to nonlinearly label human brain image data. METHODS: Each brain image voxel of each of 20 human subjects is assigned a label by each of the remaining 19 atlases using Mindboggle. The most common label is selected and is given a confidence rating based on the number of atlases that assigned that label. The automatically assigned labels for each subject brain are compared with the manual labels for that subject (its atlas). Unlike recent approaches that transform subject data to a labeled, probabilistic atlas space (constructed from a database of atlases), Mindboggle labels a subject by each atlas in a database independently. RESULTS: When Mindboggle labels a human subject's brain image with at least four atlases, the resulting label agreement with coregistered manual labels is significantly higher than when only a single atlas is used. Different numbers of atlases provide significantly higher label agreements for individual brain regions. CONCLUSION: Increasing the number of reference brains used to automatically label a human subject brain improves labeling accuracy with respect to manually assigned labels. Mindboggle software can provide confidence measures for labels based on probabilistic assignment of labels and could be applied to large databases of brain images

The serotonergic system tracks the outcomes of actions to mediate short-term motor learning

This study was supported by the Howard Hughes Medical Institute and by the Simons Foundation award 325171.To execute accurate movements, animals must continuously adapt their behavior to changes in their bodies and environments. Animals can learn changes in the relationship between their locomotor commands and the resulting distance moved, then adjust command strength to achieve a desired travel distance. It is largely unknown which circuits implement this form of motor learning, or how. Using whole-brain neuronal imaging and circuit manipulations in larval zebrafish, we discovered that the serotonergic dorsal raphe nucleus (DRN) mediates short-term locomotor learning. Serotonergic DRN neurons respond phasically to swim-induced visual motion, but little to motion that is not self-generated. During prolonged exposure to a given motosensory gain, persistent DRN activity emerges that stores the learned efficacy of motor commands and adapts future locomotor drive for tens of seconds. The DRN’s ability to track the effectiveness of motor intent may constitute a computational building block for the broader functions of the serotonergic system.Publisher PDFPeer reviewe

Two-photon calcium imaging during fictive navigation in virtual environments

A full understanding of nervous system function requires recording from large populations of neurons during naturalistic behaviors. Here we enable paralyzed larval zebrafish to fictively navigate two-dimensional virtual environments while we record optically from many neurons with two-photon imaging. Electrical recordings from motor nerves in the tail are decoded into intended forward swims and turns, which are used to update a virtual environment displayed underneath the fish. Several behavioral features—such as turning responses to whole-field motion and dark avoidance—are well-replicated in this virtual setting. We readily observed neuronal populations in the hindbrain with laterally selective responses that correlated with right or left optomotor behavior. We also observed neurons in the habenula, pallium, and midbrain with response properties specific to environmental features. Beyond single-cell correlations, the classification of network activity in such virtual settings promises to reveal principles of brainwide neural dynamics during behavior

Early treatment with intranasal neostigmine reduces mortality in a mouse model of Naja naja (Indian Cobra) envenomation

Objective. Most snakebite deaths occur prior to hospital arrival; yet inexpensive, effective, and easy to administer out-of-hospital treatments do not exist. Acetylcholinesterase inhibitors can be therapeutic in neurotoxic envenomations when administered intravenously, but nasally delivered drugs could facilitate prehospital therapy for these patients. We tested the feasibility of this idea in experimentally envenomed mice. Methods. Mice received intraperitoneal injections of Naja naja venom 2.5 to 10 times the estimated LD50 and then received

Early Treatment with Intranasal Neostigmine Reduces Mortality in a Mouse Model of Naja naja (Indian Cobra) Envenomation

Objective. Most snakebite deaths occur prior to hospital arrival; yet inexpensive, effective, and easy to administer out-of-hospital treatments do not exist. Acetylcholinesterase inhibitors can be therapeutic in neurotoxic envenomations when administered intravenously, but nasally delivered drugs could facilitate prehospital therapy for these patients. We tested the feasibility of this idea in experimentally envenomed mice. Methods. Mice received intraperitoneal injections of Naja naja venom 2.5 to 10 times the estimated LD50 and then received 5 L neostigmine (0.5 mg/mL) or 5 L normal saline by nasal administration. Animals were observed up to 12 hours and survivors were euthanized. Results. 100% of control mice died. Untreated mice injected with 2.5× LD50 Naja naja died at average 193 minutes after injection, while 10 of 15 (67%) of treated mice survived and were behaviorally normal by 6 hours ( < 0.02). In the 5× LD50 group, survival was prolonged from 45 minutes to 196 minutes ( = 0.01) and for 10× LD50 mice, survival increased from 30 to 175 minutes ( < 0.02). Conclusion. This pilot suggests that intranasal drugs can improve survival and is the first direct demonstration that such an approach is plausible, suggesting means by which treatment could be initiated before reaching the hospital. Further investigation of this approach to neurotoxic and other types of envenomation is warranted