Learning Rotational Features for Filament Detection

State-of-the-art approaches for detecting filament-like structures in noisy images rely on filters optimized for signals of a particular shape, such as an ideal edge or ridge. While these approaches are optimal when the image conforms to these ideal shapes, their performance quickly degrades on many types of real data where the image deviates from the ideal model, and when noise processes violate a Gaussian assumption

Segmentation of nerve bundles and ganglia in spine MRI using particle filters

14th International Conference, Toronto, Canada, September 18-22, 2011, Proceedings, Part IIIAutomatic segmentation of spinal nerve bundles that originate within the dural sac and exit the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves seen in high resolution myelographic MR images makes segmentation a challenging task. In this paper, we present an automatic tracking method for nerve segmentation based on particle filters. We develop a novel approach to particle representation and dynamics, based on Bézier splines. Moreover, we introduce a robust image likelihood model that enables delineation of nerve bundles and ganglia from the surrounding anatomical structures. We demonstrate accurate and fast nerve tracking and compare it to expert manual segmentation.National Institutes of Health (U.S.) (NAMIC award U54-EB005149)National Science Foundation (U.S.) (CAREER grant 0642971

Automated Delineation of Dendritic Networks in Noisy Image Stacks

We present a novel approach to 3D delineation of dendritic networks in noisy image stacks. We achieve a level of automation beyond that of state-of-the-art systems, which model dendrites as continuous tubular structures and postulate simple appearance models. Instead, we learn models from the data itself, which make them better suited to handle noise and deviations from expected appearance. From very little expert-labeled ground truth, we train both a classifier to recognize individual dendrite voxels and a density model to classify segments connecting pairs of points as dendrite-like or not. Given these models, we can then trace the dendritic trees of neurons automatically by enforcing the tree structure of the resulting graph. We will show that our approach performs better than traditional techniques on brighfield image stacks

Filter Learning for Linear Structure Segmentation

We introduce an approach to learning convolution filters whose joint output can be fed to a classifier that labels them as belonging to linear structures or not. The filters are learned using sparse synthesis techniques but we show that enforcing constraints is not required at run-time to achieve good classification performance. In practice, this is important as it drastically reduces the computational cost. We show that our approach outperforms the state-of-the-art on difficult, and very different, images of roads, retinal scans, and dendritic networks

PDGF-C Induces Maturation of Blood Vessels in a Model of Glioblastoma and Attenuates the Response to Anti-VEGF Treatment

Recent clinical trials of VEGF inhibitors have shown promise in the treatment of recurrent glioblastomas (GBM). However, the survival benefit is usually short-lived as tumors escape anti-VEGF therapies. Here we tested the hypothesis that Platelet Derived Growth Factor-C (PDGF-C), an isoform of the PDGF family, affects GBM progression independent of VEGF pathway and hinders anti-VEGF therapy.We first showed that PDGF-C is present in human GBMs. Then, we overexpressed or downregulated PDGF-C in a human GBM cell line, U87MG, and grew them in cranial windows in nude mice to assess vessel structure and function using intravital microscopy. PDGF-C overexpressing tumors had smaller vessel diameters and lower vascular permeability compared to the parental or siRNA-transfected tumors. Furthermore, vessels in PDGF-C overexpressing tumors had more extensive coverage with NG2 positive perivascular cells and a thicker collagen IV basement membrane than the controls. Treatment with DC101, an anti-VEGFR-2 antibody, induced decreases in vessel density in the parental tumors, but had no effect on the PDGF-C overexpressing tumors.These results suggest that PDGF-C plays an important role in glioma vessel maturation and stabilization, and that it can attenuate the response to anti-VEGF therapy, potentially contributing to escape from vascular normalization

Rotational Features Extraction for Ridge Detection

State-of-the-art approaches to detecting ridge-like structures in images rely on filters designed to respond to locally linear intensity features. While these approaches may be optimal for ridges whose appearance is close to being ideal, their performance degrades quickly in the presence of structured noise that corrupts the image signal, potentially to the point where it truly does not conform to the ideal model anymore. In this paper, we address this issue by introducing a learning framework that relies on rich, local, rotationally invariant image descriptors and demonstrate that we can outperform state-of-the-art ridge detectors in many different kinds of imagery. More specifically, our framework yields superior performance for the detection of blood vessel in retinal scans, dendrites in bright-field and confocal microscopy image-stacks, and streets in satellite imagery

Sensitivity of MRI Tumor Biomarkers to VEGFR Inhibitor Therapy in an Orthotopic Mouse Glioma Model

MRI biomarkers of tumor edema, vascular permeability, blood volume, and average vessel caliber are increasingly being employed to assess the efficacy of tumor therapies. However, the dependence of these biomarkers on a number of physiological factors can compromise their sensitivity and complicate the assessment of therapeutic efficacy. Here we examine the response of these MRI tumor biomarkers to cediranib, a potent vascular endothelial growth factor receptor (VEGFR) inhibitor, in an orthotopic mouse glioma model. A significant increase in the tumor volume and relative vessel caliber index (rVCI) and a slight decrease in the water apparent diffusion coefficient (ADC) were observed for both control and cediranib treated animals. This contrasts with a clinical study that observed a significant decrease in tumor rVCI, ADC and volume with cediranib therapy. While the lack of a difference between control and cediranib treated animals in these biomarker responses might suggest that cediranib has no therapeutic benefit, cediranib treated mice had a significantly increased survival. The increased survival benefit of cediranib treated animals is consistent with the significant decrease observed for cediranib treated animals in the relative cerebral blood volume (rCBV), relative microvascular blood volume (rMBV), transverse relaxation time (T2), blood vessel permeability (Ktrans), and extravascular-extracellular space (νe). The differential response of pre-clinical and clinical tumors to cediranib therapy, along with the lack of a positive response for some biomarkers, indicates the importance of evaluating the whole spectrum of different tumor biomarkers to properly assess the therapeutic response and identify and interpret the therapy-induced changes in the tumor physiology

DeepNav: Joint View Learning for Direct Optimal Path Perception in Cochlear Surgical Platform Navigation

Although much research has been conducted in the field of automated cochlear implant navigation, the problem remains challenging. Deep learning techniques have recently achieved impressive results in a variety of computer vision problems, raising expectations that they might be applied in other domains, such as identifying the optimal navigation zone (OPZ) in the cochlear. In this paper, a 2.5D joint-view convolutional neural network (2.5D CNN) is proposed and evaluated for the identification of the OPZ in the cochlear segments. The proposed network consists of 2 complementary sagittal and bird-view (or top view) networks for the 3D OPZ recognition, each utilizing a ResNet-8 architecture consisting of 5 convolutional layers with rectified nonlinearity unit (ReLU) activations, followed by average pooling with size equal to the size of the final feature maps. The last fully connected layer of each network has 4 indicators, equivalent to the classes considered: the distance to the adjacent left and right walls, collision probability and heading angle. To demonstrate this, the 2.5D CNN was trained using a parametric data generation model, and then evaluated using anatomically constructed cochlea models from the micro-CT images of different cases. Prediction of the indicators demonstrates the effectiveness of the 2.5D CNN, for example the heading angle has less than 1° error with computation delays of less that <1 milliseconds

A (Near) Real-Time Simulation Method of Aneurysm Coil Embolization

International audienceA (Near) Real-Time Simulation Method of Aneurysm Coil Embolizatio