Artificial Intelligence for Fast and Accurate 3-Dimensional Tooth Segmentation on Cone-beam Computed Tomography.

Objective: The purpose of this paper is to describe a semiautomated segmentation method for the liver and evaluate its performance on CT-scan and MR images. Methods: First, an approximate 3-D model of the liver is initialized from a few user-generated contours to globally outline the liver shape. The model is then automatically deformed by a Laplacian mesh optimization scheme until it precisely delineates the patient's liver. A correction tool was implemented to allow the user to improve the segmentation until satisfaction. Results: The proposed method was tested against 30 CT-scans from the SLIVER07 challenge repository and 20 MR studies from the Montreal University Hospital Center, covering a wide spectrum of liver morphologies and pathologies. The average volumetric overlap error was 5.1% for CT and 7.6% for MRI and the average segmentation time was 6 min. Conclusion: The obtained results show that the proposed method is efficient, reliable, and could effectively be used routinely in the clinical setting. Significance: The proposed approach can alleviate the cumbersome and tedious process of slice-wise segmentation required for precise hepatic volumetry, virtual surgery, and treatment planning.

Liver Segmentation on CT and MR Using Laplacian Mesh Optimization

To develop and evaluate a fully automatic method to measure diameters of the ascending and descending aorta on non-ECG-gated, non-contrast computed tomography (CT) scans. The method combines multi-atlas registration to obtain seed points, aorta centerline extraction, and an optimal surface segmentation approach to extract the aorta surface around the centerline. From the extracted 3D aorta segmentation, the diameter of the ascending and descending aorta was calculated at cross-sectional slices perpendicular to the extracted centerline, at the level of the pulmonary artery bifurcation, and at 1-cm intervals up to 3 cm above and below this level. Agreement with manual annotations was evaluated by dice similarity coefficient (DSC) for segmentation overlap, mean surface distance (MSD), and intra-class correlation (ICC) of diameters on 100 CT scans from a lung cancer screening trial. Repeatability of the diameter measurements was evaluated on 617 baseline-one year follow-up CT scan pairs. The agreement between manual and automatic segmentations was good with 0.95 ± 0.01 DSC and 0.56 ± 0.08 mm MSD. ICC between the diameters derived from manual and from automatic segmentations was 0.97, with the per-level ICC ranging from 0.87 to 0.94. An ICC of 0.98 for all measurements and per-level ICC ranging from 0.91 to 0.96 were obtained for repeatability. This fully automatic method can assess diameters in the thoracic aorta reliably even in non-ECG-gated, non-contrast CT scans. This could be a promising tool to assess aorta dilatation in screening and in clinical practice. • Fully automatic method to assess thoracic aorta diameters.
 • High agreement between fully automatic method and manual segmentations.
 • Method is suitable for non-ECG-gated CT and can therefore be used in screening.

/pdf/automated-3d-segmentation-and-diameter-measurement-of-the-3lhadlefrb.pdf

Automated 3D segmentation and diameter measurement of the thoracic aorta on non-contrast enhanced CT

The California Verbal Learning Test (CVLT) is recognized as a standard clinical tool for assessing episodic memory difficulties in multiple sclerosis (MS), but its neural correlates have not yet been examined in detail in this patient population. We combined neuropsychological examination and diffusion tensor imaging (DTI) analysis in a group of MS patients (N = 50) and demographically matched healthy participants (N = 20). We investigated the degree of impairment of the uncinate fascicle (UF), the superior longitudinal fascicle (SLF), the fornix (FX) and the cingulum (CG). The patients were impaired on all CVLT parameters and the DTI parameters correlated moderately with disease-related variables. Regression analyses in the complete study sample showed that CVLT learning scores correlated with impairment of the right UF. This association reached marginal significance in the patient sample. In contrast to other studies claiming retrieval deficits, our results suggest that encoding and consolidation deficits may play a major role in verbal memory impairments in MS. The findings also provide evidence for an association between degree of myelination of prefrontal fibre pathways and encoding efficiency. Finally, DTI-derived measurements appear to reflect disease progression in MS. The results are discussed in light of functional MRI studies investigating compensatory brain activity during cognitive processing in MS.

The association between California Verbal Learning Test performance and fibre impairment in multiple sclerosis: evidence from diffusion tensor imaging.

Understanding the hemodynamics of blood flow in vascular pathologies such as intracranial aneurysms is essential for both their diagnosis and treatment. Computational fluid dynamics (CFD) simulations of blood flow based on patient-individual data are performed to better understand aneurysm initiation and progression and more recently, for predicting treatment success. In virtual stenting, a flow-diverting mesh tube (stent) is modeled inside the reconstructed vasculature and integrated in the simulation. We focus on steady-state simulation and the resulting complex multiparameter data. The blood flow pattern captured therein is assumed to be related to the success of stenting. It is often visualized by a dense and cluttered set of streamlines.We present a fully automatic approach for reducing visual clutter and exposing characteristic flow structures by clustering streamlines and computing cluster representatives. While individual clustering techniques have been applied before to streamlines in 3D flow fields, we contribute a general quantitative and a domain-specific qualitative evaluation of three state-of-the-art techniques. We show that clustering based on streamline geometry as well as on domain-specific streamline attributes contributes to comparing and evaluating different virtual stenting strategies. With our work, we aim at supporting CFD engineers and interventional neuroradiologists.

Blood Flow Clustering and Applications inVirtual Stenting of Intracranial Aneurysms

Visual Computing in Biology and Medicine: Interactive 3D medical image segmentation with energy-minimizing implicit functions

We introduce a novel technique that allows for an automatic quantification of MR DTI parameters along arbitrarily oriented fiber bundles. Most previous methods require either a manual placement of ROIs, are limited to single fiber tracts, or are limited to bundles which are perpendicular to one of the three image planes. Thus, the quantification process is made much more time-efficient and robust by our new approach. We compare our technique with a manual quantification of an expert and show the similarity of the results. Furthermore, we demonstrate how to visualize the parameters at a certain position of the fiber bundle so that areas of interest can easily be examined.

/pdf/automatic-quantification-of-dti-parameters-along-fiber-474w7l4iym.pdf

Automatic Quantification of DTI Parameters Along Fiber Bundles

We introduce novel data structures and algorithms for clustering white matter fiber tracts to improve accuracy
and robustness of existing techniques. Our novel fiber grid combined with a new randomized soft-division
algorithm allows for defining the fiber similarity more precisely and efficiently than a feature space. A fine-tuning
of several parameters to a particular fiber set - as it is often required if using a feature space - becomes obsolete.
The idea is to utilize a 3D grid where each fiber point is assigned to cells with a certain weight. From this grid, an
affinity matrix representing the fiber similarity can be calculated very efficiently in time O( n ) in the average case,
where n denotes the number of fibers. This is superior to feature space methods which need O( n 2 ) time. Our novel
eigenvalue regression is capable of determining a reasonable number of clusters as it accounts for inter-cluster
connectivity. It performs a linear regression of the eigenvalues of the affinity matrix to find the point of maximum
curvature in a list of descending order. This allows for identifying inner clusters within coarse structures, which
automatically and drastically reduces the a-priori knowledge required for achieving plausible clustering results.
Our extended multiple eigenvector clustering exhibits a drastically improved robustness compared to the well-known
elongated clustering, which also includes an automatic detection of the number of clusters. We present
several examples of artificial and real fiber sets clustered by our approach to support the clinical suitability and
robustness of the proposed techniques.

Grid-based spectral fiber clustering

We present a novel software assistant that unlocks new potentials in neurosurgical planning and risk assessment. It allows surgeons to approach the task in an intuitive manner, by providing them with the possibility to simultaneously observe all relevant data of a case in synchronized 2D and 3D views. State-of-the-art technologies from the field of computer graphics are combined to allow simultaneous interactive rendering of anatomical and functional MR data in combination with manually segmented objects and slice-based overlays. This allows surgeons to perceive a clearer impression of the anatomical and functional structures affected by an intervention, and especially the way they are related to each other. Thus, it significantly facilitates the finding of an optimal intervention strategy.

/pdf/state-of-the-art-computer-graphics-in-neurosurgical-planning-2itmwyvw8o.pdf

State-of-the-Art Computer Graphics in Neurosurgical Planning and Risk Assessment

We present a fast and interactive segmentation method for medical images that allows a smooth reconstruction of an object's surface from a set of user drawn, three-dimensional, planar contours that can be arbitrarily oriented Our algorithm uses an interpolation based on variational implicit functions

Because variational interpolation is computationally expensive, we show how to speed up the algorithm to achieve an interactive calculation time while preserving the overall segmentation quality The performance improvements are based on a quality preserving reduction of the number of contour points and a fast voxelization strategy for the resulting implicit function A huge speedup is achieved by the parallelization of the algorithms, utilizing modern 64-bit multi-core CPUs Finally, we discuss how to make the interpolation algorithm more robust to selfintersecting and reduced contours

Fast and smooth interactive segmentation of medical images using variational interpolation

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