Computational neuroanatomy: ontology-based representation of neural components and connectivity

Background: A critical challenge in neuroscience is organizing, managing, and accessing the explosion in neuroscientific knowledge, particularly anatomic knowledge. We believe that explicit knowledge-based approaches to make neuroscientific knowledge computationally accessible will be helpful in tackling this challenge and will enable a variety of applications exploiting this knowledge, such as surgical planning. Results: We developed ontology-based models of neuroanatomy to enable symbolic lookup, logical inference and mathematical modeling of neural systems. We built a prototype model of the motor system that integrates descriptive anatomic and qualitative functional neuroanatomical knowledge. In addition to modeling normal neuroanatomy, our approach provides an explicit representation of abnormal neural connectivity in disease states, such as common movement disorders. The ontology-based representation encodes both structural and functional aspects of neuroanatomy. The ontology-based models can be evaluated computationally, enabling development of automated computer reasoning applications. Conclusion: Neuroanatomical knowledge can be represented in machine-accessible format using ontologies. Computational neuroanatomical approaches such as described in this work could become a key tool in translational informatics, leading to decision support applications that inform and guide surgical planning and personalized care for neurological disease in the future

Cross Sectional Atlas of the Brain

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Hybrid Formulation of the Model-Based Non-Rigid Registration Problem to Improve Accuracy and Robustness

International audienceno abstrac

Robust Non-Rigid Registration to Capture Brain Shift from Intra-Operative MRI

International audienceWe present a new algorithm to register 3D pre-operative Magnetic Resonance (MR) images to intra-operative MR images of the brain which have undergone brain shift. This algorithm relies on a robust estimation of the deformation from a sparse noisy set of measured displacements. We propose a new framework to co mpute the displacement field in an iterative process, allowing the solution to gradually move from an approximation formulation (minimizing the sum of a re gularization term and a data error term) to an interpolation formulation (least square minimization of the data error term). An outlier rejection step is i ntroduced in this gradual registration process using a weighted least trimmed squares approach, aiming at improving the robustness of the algorithm. We use a patient-specific model discretized with the finite element method (FEM) in order to ensure a realistic mechanical behavior of the brain tissue. To meet the clinical time constraint, we parallelized the slowest step of the algorithm so that we can perform a full 3D image registration in 35 seconds ( including the image update time) on a heterogeneous cluster of 15 PCs. The algorithm has been tested on six cases of brain tumor resection, presenting a brain shift of up to 14 mm. The results show a good ability to recover la rge displacements, and a limited decrease of accuracy near the tumor resection cavity

A New Metric for Detecting Change in Slowly Evolving Brain Tumors: Validation in Meningioma Patients

BACKGROUND: Change detection is a critical component in the diagnosis and monitoring of many slowly evolving pathologies. OBJECTIVE: This article describes a semiautomatic monitoring approach using longitudinal medical images. We test the method on brain scans of patients with meningioma, which experts have found difficult to monitor because the tumor evolution is very slow and may be obscured by artifacts related to image acquisition. METHODS: We describe a semiautomatic procedure targeted toward identifying difficult- to-detect changes in brain tumor imaging. The tool combines input from a medical expert with state-of-the-art technology. The software is easy to calibrate and, in less than 5 minutes, returns the total volume of tumor change in mm3. We test the method on postgadolinium, T1-weighted magnetic resonance images of 10 patients with meningioma and compare our results with experts' findings. We also perform benchmark testing with synthetic data. RESULTS: Our experiments indicated that experts' visual inspections are not sensitive enough to detect subtle growth. Measurements based on experts' manual segmentations were highly accurate but also labor intensive. The accuracy of our approach was comparable to the experts' results. However, our approach required far less user input and generated more consistent measurements. CONCLUSION: The sensitivity of experts' visual inspection is often too low to detect subtle growth of meningiomas from longitudinal scans. Measurements based on experts' segmentation are highly accurate but generally too labor intensive for standard clinical settings. We described an alternative metric that provides accurate and robust measurements of subtle tumor changes while requiring a minimal amount of user input

A New Metric for Detecting Change in Slowly Evolving Brain Tumors: Validation in Meningioma Patients

BACKGROUND: Change detection is a critical component in the diagnosis and monitoring of many slowly evolving pathologies. OBJECTIVE: This article describes a semiautomatic monitoring approach using longitudinal medical images. We test the method on brain scans of patients with meningioma, which experts have found difficult to monitor because the tumor evolution is very slow and may be obscured by artifacts related to image acquisition. METHODS: We describe a semiautomatic procedure targeted toward identifying difficult-to-detect changes in brain tumor imaging. The tool combines input from a medical expert with state-of-the-art technology. The software is easy to calibrate and, in less than 5 minutes, returns the total volume of tumor change in mm. We test the method on postgadolinium, T1-weighted magnetic resonance images of 10 patients with meningioma and compare our results with experts' findings. We also perform benchmark testing with synthetic data. RESULTS: Our experiments indicated that experts' visual inspections are not sensitive enough to detect subtle growth. Measurements based on experts' manual segmentations were highly accurate but also labor intensive. The accuracy of our approach was comparable to the experts' results. However, our approach required far less user input and generated more consistent measurements. CONCLUSION: The sensitivity of experts' visual inspection is often too low to detect subtle growth of meningiomas from longitudinal scans. Measurements based on experts' segmentation are highly accurate but generally too labor intensive for standard clinical settings. We described an alternative metric that provides accurate and robust measurements of subtle tumor changes while requiring a minimal amount of user input

Method for Combining Information from White Matter Fiber Tracking and Gray

Summary: We introduce a method for combining fiber tracking from diffusion-tensor (DT) imaging with cortical gray matter parcellation from structural high-spatialresolution 3D spoiled gradient-recalled acquisition in the steady state images. We applied this method to a tumor case to determine the impact of the tumor on white matter architecture. We conclude that this new method for combining structural and DT imaging data is useful for understanding cortical connectivity and the localization of fiber tracts and their relationship with cortical anatomy and brain abnormalities. Diffusion tensor (DT) imaging (1) makes possible the visualization and appreciation of white matter tracts in the brain and provides important information about brain connectivity. DT images, however