We have witnessed recently a huge explosion of human brain big projects. Despite these immense efforts and acquisitions of enormous amounts of brain data, a model of the human whole brain at the nanoscale has not yet been developed. The goal of this work is to estimate high performance computing (HPC) requirements needed for morphology modeling of the entire human brain at the nanoscale including the segmentation of neurons and the calculation of chemical and electrical synapses.These requirements are estimated for different computers (1 giga-, 1 tera-, 1 peta-, and 1 exa FLOPS) and various spatial resolutions of volumetric brain data (1 µm as the reference, 0.5 µm to handle axons and large dendrites, 10 nm to handle chemical synapses, and 1 nm to handle electrical synapses). The determined numbers of operations are the lower-bound estimates needed for segmenting neurons by region growing and calculating synapses by multi-directional sampling.At the 1 µm spatial resolution, a giga FLOPS computer needs more than 2 years to segment the neurons and only a tera FLOPS (taking over 18 hours) or higher supercomputers are suitable for this task. At the 10 nm resolution, an exascale supercomputer is able to segment the neurons in no less than 18 hours (and at present there is only one operating exascale supercomputer in the world, Frontier; two more exascale supercomputers Aurora and El Capitan are under installation). The HPC requirements for the calculation of chemical synapses are negligible in comparison to those for neuron segmentation. To segment the neurons at 1 nm level enabling the calculation of electrical synapses, an exascale supercomputer requires more than 2 years.To my best knowledge, this is the first attempt to estimate the HPC requirements to compute a morphology model of the human whole brain at the nanoscale.

Due to its importance and possible applications in visualization, tumor detection and preoperative planning, automatic small bowel segmentation is essential for computer-aided diagnosis of small bowel pathology. However, segmenting the small bowel directly on computed tomography (CT) scans is very difficult because of the low image contrast on CT scans and high tortuosity of the small bowel and its close proximity to other abdominal organs. Motivated by the intensity characteristics of abdominal CT images, the anatomic relationship between the mesenteric vasculature and the small bowel, and potential usefulness of the mesenteric vasculature for establishing the path of the small bowel, we propose a novel mesenteric vasculature map-guided method for small bowel segmentation on high-resolution CT angiography scans. The major mesenteric arteries are first segmented using a vessel tracing method based on multi-linear subspace vessel model and Bayesian inference. Second, multi-view, multi-scale vesselness enhancement filters are used to segment small vessels, and vessels directly or indirectly connecting to the superior mesenteric artery are classified as mesenteric vessels. Third, a mesenteric vasculature map is built by linking vessel bifurcation points, and the small bowel is segmented by employing the mesenteric vessel map and fuzzy connectness. The method was evaluated on 11 abdominal CT scans of patients suspected of having carcinoid tumors with manually labeled reference standard. The result, 82.5% volume overlap accuracy compared with the reference standard, shows it is feasible to segment the small bowel on CT scans using the mesenteric vasculature as a roadmap.

The existing differential approaches for localization of 3-D anatomic point landmarks in 3-D images are sensitive to noise and usually extract numerous spurious landmarks. The parametric model-based approaches are not practically usable for localization of landmarks that can not be modeled by simple parametric forms. Some dedicated methods using anatomic knowledge to identify particular landmarks are not general enough to cope with other landmarks. In this paper, we propose a model-based, semi-global segmentation approach to automatically localize 3-D point landmarks in neuroimages. To localize a landmark, the semi-global segmentation (meaning the segmentation of a part of the studied structure in a certain neighborhood of the landmark) is first achieved by an active surface model, and then the landmark is localized by analyzing the segmented part only. The joint use of global model-to-image registration, semi-global structure registration, active surface-based segmentation, and point-anchored surface registration makes our method robust to noise and shape variation. To evaluate the method, we apply it to the localization of ventricular landmarks including curvature extrema, centerline intersections, and terminal points. Experiments with 48 clinical and 18 simulated magnetic resonance (MR) volumetric images show that the proposed approach is able to localize these landmarks with an average accuracy of 1 mm (i.e., at the level of image resolution). We also illustrate the use of the proposed approach to cortical landmark identification and discuss its potential applications ranging from computer-aided radiology and surgery to atlas registration with scans.

We describe a process aiming to construct a 3-D geometric model of the human normal intracranial venous system from MRA data. An analysis of geometric properties of the intracranial veins and sinuses results in proposing three models: circular, elliptic, and free-shape. We formulate a rule based on which a suitable geometric venous model can be selected. The cross-sectional shape of different parts of dural venous sinuses is found to be better approximated by ellipses and free shapes, while for veins the circular and elliptic models are comparable. An analysis of using splines for radii smoothing is also provided. The approach is useful for building venous models in education and clinical applications.

This paper proposes a novel model-guided segmentation framework utilizing a statistical surface wavelet model as a shape prior. In the model building process, a set of training shapes are decomposed through the subdivision surface wavelet scheme. By interpreting the resultant wavelet coefficients as random variables, we compute prior probability distributions of the wavelet coefficients to model the shape variations of the training set at different scales and spatial locations. With this statistical shape model, the segmentation task is formulated as an optimization problem to best fit the statistical shape model with an input image. Due to the localization property of the wavelet shape representation both in scale and space, this multi-dimensional optimization problem can be efficiently solved in a multiscale and spatial-localized manner. We have applied our method to segment cerebral caudate nuclei from MRI images. The experimental results have been validated with segmentations obtained through human expert. These show that our method is robust, computationally efficient and achieves a high degree of segmentation accuracy.

Abstract Collision detection is highly important in computer graphics and virtual reality. Most collision detection methods are object‐based, relying on testing the geometrical interference of objects, and their performance therefore depends on the geometrical complexity of the objects. Recently, image‐based methods have gained increasing acceptance for their simplicity in implementation, robustness with respect to the object geometry, and the potential to distribute the computational burden onto graphics hardware. However, all existing image‐based methods require direct calls to OpenGL, but so far there is no direct way to access OpenGL through the Java 3D API. Although Java 3D provides its own built‐in collision detection classes, they are either incorrect or inefficient. In this paper, we present a hybrid image‐based collision detection method in Java 3D, which incorporates the Java 3D built‐in collision detection and the image‐based collision detection in our specially devised scene graph. In addition, we take advantage of the fact that the 3D position of successive offscreen views (i.e. virtual views perceived by the probing object) does not change significantly and thereby reduce the occurrences of offscreen rendering, so that the collision detection becomes even faster (up to 50% in our case). Experimental results prove the correctness and efficiency of our method. Copyright © 2006 John Wiley & Sons, Ltd.

A fast and robust algorithm to segment the head from T1-weighted magnetic resonance (MR) volumes is presented based on robust determination of threshold from combination of the original volume (through employing the fuzzy c-means clustering which is insensitive to intensity inhomogeneity) and the average axial image (which suppresses the intensity noise), efficient connected component analysis through iterative recursion and fast implementation of morphological operations via decomposition of structuring elements. The algorithm has been validated against 101 data sets with various artifacts

In this paper we modify radial basis functions enabling mapping of images by preserving a specific segment on the transforming space and aligning given surfaces on images. Our mapping functions are derived from the concept of the classical radial basis functions overcoming some of their shortcomings. Unlike the classical functions which are restricted to point set of landmarks only, our functions are able to handle surface, contour, and point landmarks. The criterion for our functions is that they must be continuous and provide one-to-one mapping. We align the given landmarks with our functions by shifting points along rays normal to the given specific surface. This approach allows transforming brain images preserving positions of the anterior and posterior commissures (AC-PC) as well as the midsagittal plane. The proposed method can be useful in brain warping for analysis of volumetric and two-dimensional scans

This paper presents a method to improve the semi-automatic method for intensity inhomogeneity correction by Dawant et al. through introducing a fully automatic approach to reference points generation, which is based on order statistics and integrates information from the fine to coarse scale representations of the input image. The method has been validated and compared with two popular methods, N3 and BFC. Advantages of the proposed method are demonstrated.

A novel thresholding approach to confine the intensity frequency range of the object based on supervision is introduced. It consists of three steps. First, the region of interest (ROI) is determined in the image. Then, from the histogram of the ROI, the frequency range in which the proportion of the background to the ROI varies is estimated through supervision. Finally, the threshold is determined by minimizing the classification error within the constrained variable background range. The performance of the approach has been validated against 54 brain MR images, including images with severe intensity inhomogeneity and/or noise, CT chest images, and the Cameraman image. Compared with nonsupervised thresholding methods, the proposed approach is substantially more robust and more reliable. It is also computationally efficient and can be applied to a wide class of computer vision problems, such as character recognition, fingerprint identification, and segmentation of a wide variety of medical images.

We propose an anatomy-based approach for an efficient construction of a three-dimensional human normal cerebral arterial model from segmented and skeletonized angiographic data. The centerline-based model is used for an accurate angiographic data representation. A vascular tree is represented by tubular segments and bifurcations whose construction takes into account vascular anatomy. A bifurcation is defined quantitatively and the algorithm calculating it is given. The centerline is smoothed by means of a sliding average filter. As the vessel radius is sensitive to quality of data as well as accuracy of segmentation and skeletonization, radius outlier removal and radius regression algorithms are formulated and applied. In this way, the approach compensates for some inaccuracies introduced during segmentation and skeletonization. To create the frame of vasculature, we use two different topologies: tubular and B-subdivision based. We also propose a technique to prevent vessel twisting. The analysis of the vascular model is done on a variety of data containing 258 vascular segments and 131 bifurcations. Our approach gives acceptable results from anatomical, topological and geometrical standpoints as well as provides fast visualization and manipulation of the model. The approach is applicable for building a reference cerebrovascular atlas, developing applications for simulation and planning of interventional radiology procedures and vascular surgery, and in education.

An algorithm to locate the four modified Talairach cortical landmarks (A, P, L, and R) based on knowledge is proposed. Knowledge is employed to select the axial plane (the AP plane) passing through the anterior and posterior commissures, determination of optimum thresholds, segmentation, and refinement of the AP plane. The algorithm has been validated against 38 T1-weighted datasets from public resources. It takes less than 1 second on Pentium 4 (2.6 GHz) to extract the 4 landmarks. The average landmark location error is below 1 mm. The algorithm is robust and accurate as the factors influencing the determination of cortical landmarks like noise, gray level inhomogeneity, partial volume effect, and connection between the cortex and the sagittal sinus/meninges/skull are carefully compensated. A low computational cost is attributed to simple operations like thresholding, seeding, simple morphological operations and distance transform.

Efficient tools to visualize, browse and quantify patient-specific data as well as more user-friendly and powerful ways of interacting with these by virtual reality (VR) are elaborated. VR applications for brain intervention have been developed and are discussed in the paper.

In this paper, we propose an automatic method to segment the Sylvian fissure (SF) from T1-weighted magnetic resonance (MR) images. The method consists of four steps. First, morphological methods are applied to remove non-brain tissues. Then, 2D region growing is used to segment the cerebral spinal fluid (CSF) and gray matter (GM) in the SF, in which spatial relationships are used to control the region growing. A modified fuzzy c-means (FCM) clustering algorithm is introduced as well, which is more accurate than the conventional FCM method. After that, we get the 3D volume of the SF through segmenting CSF and GM slice by slice. Finally, CSF in the SF is extracted incorporating the anatomical information on a constant cortex thickness. We have demonstrated our method through both phantom and patient data.

A system supporting an interventional radiology (IR) procedure is presented in this paper. A 3D geometric modeling method has been developed to construct a 3D vascular model containing complete connection information of cerebral vasculature for IR planning and intra-operative guidance. The validation result proved the fidelity of the reconstructed model. A magnetic tracking system is employed in this project to trace the tip of the IR device, whose model can be displayed onto a 3D digital model in real time. Magnetic tracking of IR device plus the 3D model can potentially help the radiologist more easily manipulate IR device through vasculature. A method following automatic guidance of IR device (such as catheter and guidewire) from the entry to target is proposed, which can reduce the radiation dose and thus benefit both patients and staff.

This paper presents a knowledge-driven algorithm to identify and segment the central sulcus (CS) from human brain MR images. The dataset is reformatted along the anterior and posterior commissures (AC-PC) plane first. Then, the 3D region within the two coronal planes passing through the AC and PC is defined as the region of interest (ROl) to search for all the sulci within it. The CS is the sulcus with the largest volume within the ROI. Together with the sulci, grey matter (GM) is included for the region growing in order to deal with the partial volume effect. The GM is removed through skeletonization. Experimental results are given.

This paper introduces a computer-aided atlas-based functional neurosurgery methodology and describes NeuroPlanner, a software system which supports it. NeuroPlanner provides four groups of functions: 1) data-related for data reading, interpolation, reformatting, and image processing; 2) atlas-related for multiple atlases reading, atlas-to-data global and local registrations, two-way anatomical indexing, and multiple labeling in two and three dimensions; 3) atlas-data exploration-related for three-dimensional (3-D) display and real-time manipulation of cerebral structures, continuous navigation, two-dimensional (2-D), triplanar, 3-D presentations, and 2-D interaction in four views; and 4) neurosurgery-related for targeting, trajectory planning, mensuration, simulating the insertion of microelectrode, and simulating therapeutic lesioning. All operations, excluding atlas and data reading, are real time. The combined anatomical index of the multiple brain atlas database containing complementary 2-D and 3-D atlases has about 1000 structures per hemisphere, and over 400 sulcal patterns. Neurosurgical planning with mutually preregistered multiple brain atlases in all three orthogonal orientations is novel. The approach is validated with 24 intraoperative and postoperative datasets for thalamotomies, thalamic stimulations, pallidotomies, and pallidal stimulations. Its potential benefits include increased accuracy of target definition, reduced time of the surgical procedure by decreasing the number of tracts, facilitated planning of more sophisticated trajectories, lowered cost by reducing the number of microelectrodes used, reduced surgical complications, and the extra degree of confidence given to the neurosurgeon.

We integrate an electronic atlas of brain structure with a virtual stereotactic frame in a system for intuitive, reach-in manipulation. Clinical study now under way will test our expectation that plans can be prepared faster, with better and more accurate choice of target points, improved avoidance of sensitive structures, fewer abortive frame attachments, and speedier, more effective training. All these factors, if validated, will increase medical efficacy and reduce costs.