Fully Automatic Segmentation of Brain Tumor Images using Support Vector Machine Classiffication in Combination with Hierarchical Conditional Random Field Regularization

Delineating brain tumor boundaries from magnetic resonance images is an essential task for the analysis of brain cancer. We propose a fully automatic method for brain tissue segmentation, which combines Support Vector Machine classification using multispectral intensities and textures with subsequent hierarchical regularization based on Conditional Random Fields. The CRF regularization introduces spatial constraints to the powerful SVM classification, which assumes voxels to be independent from their neighbors. The approach first separates healthy and tumor tissue before both regions are subclassified into cerebrospinal fluid, white matter, gray matter and necrotic, active, edema region respectively in a novel hierarchical way. The hierarchical approach adds robustness and speed by allowing to apply different levels of regularization at different stages. The method is fast and tailored to standard clinical acquisition protocols. It was assessed on 10 multispectral patient datasets with results outperforming previous methods in terms of segmentation detail and computation times.

Solution of the relativistic Schrödinger equation for the δ ′ -Function potential in one dimension using cutoff regularization

We study the relativistic version of the Schrödinger equation for a point particle in one dimension with the potential of the first derivative of the delta function. The momentum cutoff regularization is used to study the bound state and scattering states. The initial calculations show that the reciprocal of the bare coupling constant is ultraviolet divergent, and the resultant expression cannot be renormalized in the usual sense, where the divergent terms can just be omitted. Therefore, a general procedure has been developed to derive different physical properties of the system. The procedure is used first in the nonrelativistic case for the purpose of clarification and comparisons. For the relativistic case, the results show that this system behaves exactly like the delta function potential, which means that this system also shares features with quantum filed theories, like being asymptotically free. In addition, in the massless limit, it undergoes dimensional transmutation, and it possesses an infrared conformal fixed point. The comparison of the solution with the relativistic delta function potential solution shows evidence of universality.

AICAR and metformin, but not exercise, increase muscle glucose transport through AMPK-, ERK-, and PDK1-dependent activation of atypical PKC

Activators of 5'-AMP-activated protein kinase (AMPK) 5-aminoimidazole-4-carboxamide-1-beta-d-ribofuranoside (AICAR), metformin, and exercise activate atypical protein kinase C (aPKC) and ERK and stimulate glucose transport in muscle by uncertain mechanisms. Here, in cultured L6 myotubes: AICAR- and metformin-induced activation of AMPK was required for activation of aPKC and ERK; aPKC activation involved and required phosphoinositide-dependent kinase 1 (PDK1) phosphorylation of Thr410-PKC-zeta; aPKC Thr410 phosphorylation and activation also required MEK1-dependent ERK; and glucose transport effects of AICAR and metformin were inhibited by expression of dominant-negative AMPK, kinase-inactive PDK1, MEK1 inhibitors, kinase-inactive PKC-zeta, and RNA interference (RNAi)-mediated knockdown of PKC-zeta. In mice, muscle-specific aPKC (PKC-lambda) depletion by conditional gene targeting impaired AICAR-stimulated glucose disposal and stimulatory effects of both AICAR and metformin on 2-deoxyglucose/glucose uptake in muscle in vivo and AICAR stimulation of 2-[(3)H]deoxyglucose uptake in isolated extensor digitorum longus muscle; however, AMPK activation was unimpaired. In marked contrast to AICAR and metformin, treadmill exercise-induced stimulation of 2-deoxyglucose/glucose uptake was not inhibited in aPKC-knockout mice. Finally, in intact rodents, AICAR and metformin activated aPKC in muscle, but not in liver, despite activating AMPK in both tissues. The findings demonstrate that in muscle AICAR and metformin activate aPKC via sequential activation of AMPK, ERK, and PDK1 and the AMPK/ERK/PDK1/aPKC pathway is required for metformin- and AICAR-stimulated increases in glucose transport. On the other hand, although aPKC is activated by treadmill exercise, this activation is not required for exercise-induced increases in glucose transport, and therefore may be a redundant mechanism.

Femur Specific Polyaffine Model to Regularize the Log-Domain Demons Registration

Osteoarticular allograft transplantation is a popular treatment method in wide surgical resections with large defects. For this reason hospitals are building bone data banks. Performing the optimal allograft selection on bone banks is crucial to the surgical outcome and patient recovery. However, current approaches are very time consuming hindering an efficient selection. We present an automatic method based on registration of femur bones to overcome this limitation. We introduce a new regularization term for the log-domain demons algorithm. This term replaces the standard Gaussian smoothing with a femur specific polyaffine model. The polyaffine femur model is constructed with two affine (femoral head and condyles) and one rigid (shaft) transformation. Our main contribution in this paper is to show that the demons algorithm can be improved in specific cases with an appropriate model. We are not trying to find the most optimal polyaffine model of the femur, but the simplest model with a minimal number of parameters. There is no need to optimize for different number of regions, boundaries and choice of weights, since this fine tuning will be done automatically by a final demons relaxation step with Gaussian smoothing. The newly developed synthesis approach provides a clear anatomically motivated modeling contribution through the specific three component transformation model, and clearly shows a performance improvement (in terms of anatomical meaningful correspondences) on 146 CT images of femurs compared to a standard multiresolution demons. In addition, this simple model improves the robustness of the demons while preserving its accuracy. The ground truth are manual measurements performed by medical experts.

Geometry-Aware Multiscale Image Registration via OBBTree-Based Polyaffine Log-Demons

Non-linear image registration is an important tool in many areas of image analysis. For instance, in morphometric studies of a population of brains, free-form deformations between images are analyzed to describe the structural anatomical variability. Such a simple deformation model is justified by the absence of an easy expressible prior about the shape changes. Applying the same algorithms used in brain imaging to orthopedic images might not be optimal due to the difference in the underlying prior on the inter-subject deformations. In particular, using an un-informed deformation prior often leads to local minima far from the expected solution. To improve robustness and promote anatomically meaningful deformations, we propose a locally affine and geometry-aware registration algorithm that automatically adapts to the data. We build upon the log-domain demons algorithm and introduce a new type of OBBTree-based regularization in the registration with a natural multiscale structure. The regularization model is composed of a hierarchy of locally affine transformations via their logarithms. Experiments on mandibles show improved accuracy and robustness when used to initialize the demons, and even similar performance by direct comparison to the demons, with a significantly lower degree of freedom. This closes the gap between polyaffine and non-rigid registration and opens new ways to statistically analyze the registration results.

Personalized X-Ray Reconstruction of the Proximal Femur via Intensity-Based Non-rigid 2D-3D Registration

This paper presents a new approach for reconstructing a patient-specific shape model and internal relative intensity distribution of the proximal femur from a limited number (e.g., 2) of calibrated C-arm images or X-ray radiographs. Our approach uses independent shape and appearance models that are learned from a set of training data to encode the a priori information about the proximal femur. An intensity-based non-rigid 2D-3D registration algorithm is then proposed to deformably fit the learned models to the input images. The fitting is conducted iteratively by minimizing the dissimilarity between the input images and the associated digitally reconstructed radiographs of the learned models together with regularization terms encoding the strain energy of the forward deformation and the smoothness of the inverse deformation. Comprehensive experiments conducted on images of cadaveric femurs and on clinical datasets demonstrate the efficacy of the present approach.

Vaccination-induced functional competence of circulating human tumor-specific CD8 T-cells

T-cells specific for foreign (e.g., viral) antigens can give rise to strong protective immune responses, whereas self/tumor antigen-specific T-cells are thought to be less powerful. However, synthetic T-cell vaccines composed of Melan-A/MART-1 peptide, CpG and IFA can induce high frequencies of tumor-specific CD8 T-cells in PBMC of melanoma patients. Here we analyzed the functionality of these T-cells directly ex vivo, by multiparameter flow cytometry. The production of multiple cytokines (IFNγ, TNFα, IL-2) and upregulation of LAMP-1 (CD107a) by tumor (Melan-A/MART-1) specific T-cells was comparable to virus (EBV-BMLF1) specific CD8 T-cells. Furthermore, phosphorylation of STAT1, STAT5 and ERK1/2, and expression of CD3 zeta chain were similar in tumor- and virus-specific T-cells, demonstrating functional signaling pathways. Interestingly, high frequencies of functionally competent T-cells were induced irrespective of patient's age or gender. Finally, CD8 T-cell function correlated with disease-free survival. However, this result is preliminary since the study was a Phase I clinical trial. We conclude that human tumor-specific CD8 T-cells can reach functional competence in vivo, encouraging further development and Phase III trials assessing the clinical efficacy of robust vaccination strategies.

ANOVA kernels and RKHS of zero mean functions for model-based sensitivity analysis

Given a reproducing kernel Hilbert space (H,〈.,.〉)(H,〈.,.〉) of real-valued functions and a suitable measure μμ over the source space D⊂RD⊂R, we decompose HH as the sum of a subspace of centered functions for μμ and its orthogonal in HH. This decomposition leads to a special case of ANOVA kernels, for which the functional ANOVA representation of the best predictor can be elegantly derived, either in an interpolation or regularization framework. The proposed kernels appear to be particularly convenient for analyzing the effect of each (group of) variable(s) and computing sensitivity indices without recursivity.

Indocyanine Green Loaded Biocompatible Nanoparticles: Stabilisation of Indocyanine Green (ICG) Using Biocompatible Silica-Poly (&epsilon-Caprolactone) Grafted Nanocomposites

Indocyanine green (ICG) is a chemically labile compound which needs to be stabilized in aqueous media to be used in biomedical applications. In the present study, poly(ε-caprolactone) (PCL), a semi-crystalline polyester, was used to encapsulate and stabilize ICG in a hydrophobic environment. A hydrophobic and biocompatible nanocomposite was obtained by the process of encapsulating inorganic silica. ICG was embedded in the hydrophobic polymer coating by starting from a well-defined silica (Si) core of either 80 nm or 120 nm diameter, which served as a template for a ‘grafting from’ approach using ε-caprolactone. The obtained nanocomposite Si grafted PCL/ICG was based on silica nanoparticles grafted with PCL, in which ICG was adsorbed. The nanoparticles were characterized by IR spectroscopy, thermogravimetric analysis (TGA) and scanning electron microscopy (SEM). The change in the surface charge and the colloidal stability of the nanoparticles was followed by zeta potential measurements. This approach of synthesizing nanocomposite-based ICG demonstrates a new route to stabilize ICG. We synthesized biocompatible nanoparticles containing a high ICG concentration and exhibiting excellent stability to aqueous decomposition.

A Hybrid Model for Multimodal Brain Tumor Segmentation

We present a fully automatic segmentation method for multi-modal brain tumor segmentation. The proposed generative-discriminative hybrid model generates initial tissue probabilities, which are used subsequently for enhancing the classi�cation and spatial regularization. The model has been evaluated on the BRATS2013 training set, which includes multimodal MRI images from patients with high- and low-grade gliomas. Our method is capable of segmenting the image into healthy (GM, WM, CSF) and pathological tissue (necrotic, enhancing and non-enhancing tumor, edema). We achieved state-of-the-art performance (Dice mean values of 0.69 and 0.8 for tumor subcompartments and complete tumor respectively) within a reasonable timeframe (4 to 15 minutes).

Asymptotic freedom, dimensional transmutation, and an infrared conformal fixed point for the δ-function potential in one-dimensional relativistic quantum mechanics

We consider the Schrödinger equation for a relativistic point particle in an external one-dimensional δ-function potential. Using dimensional regularization, we investigate both bound and scattering states, and we obtain results that are consistent with the abstract mathematical theory of self-adjoint extensions of the pseudodifferential operator H=p2+m2−−−−−−−√. Interestingly, this relatively simple system is asymptotically free. In the massless limit, it undergoes dimensional transmutation and it possesses an infrared conformal fixed point. Thus it can be used to illustrate nontrivial concepts of quantum field theory in the simpler framework of relativistic quantum mechanics.