Semi-Automated Reconstruction of Curvilinear Structures in Noisy 2D images and 3D image stacks

We propose a new approach to semi-automated delineation of curvilinear structures in a wide range of imaging modalities. Earlier approaches lack robustness to imaging noise, do not provide radius estimates for the structures and operate only on single channel images. In contrast, ours makes use of the color information, when available, and generates accurate centreline location and radius estimates with minimal supervision. We demonstrate this on a wide range of datasets ranging from a 2D dataset of aerial images to 3D micrographs of neurites

Distributed under Creative Commons Attribution License Contents 1 Intensity enhancement 2

We present a semi-automatic algorithm for Carotid lumen segmentation on CTA images. Our method is based on a variant of the minimal path method that models the vessel as a centerline and boundary. This is done by adding one dimension for the local radius around the centerline. The crucial step of our method is the definition of the local metrics to minimize. We have chosen to exploit the tubular structure of the vessels one wants to extract to built an anisotropic metric giving higher speed on the center of the vessels and also when the minimal path tangent is coherent with the vessels direction. Due to carotid stenosis or occlusions on the provided data, segmentation is refined using a region-based level sets

Automated quantification of morphodynamics for high-throughput live cell time-lapse dataset

We present a fully automatic method to track and quantify the morphodynamics of differentiating neurons in uorescence time-lapse microscopy datasets. While previous efforts have successfully quantified the dynamics of organelles such as the cell body, nucleus, or chromosomes of cultured cells, neurons have proved to be uniquely challenging due to their highly deformable neurites which expand, branch, and collapse. Our approach is capable of robustly detecting, tracking, and segmenting all the components of each neuron present in the sequence including the nucleus, soma, neurites, and filopodia. To meet the demands required for high-throughput processing, our framework is designed tobe extremely effcient, capable of processing a single image in approximately two seconds on a conventional notebook computer. For validation of our approach, we analyzed neuronal differentiation datasets in which a set of genes was perturbed using RNA interference. Our analysis confirms previous quantitative findings measured from static images, as well as previous qualitative observations of morphodynamic phenotypes that could not be measured on a large scale. Finally, we present new observations about the behavior of neurons made possible by our quantitative analysis, which are not immediately obvious to a human observer

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

Reconstructing Curvilinear Networks using Path Classifiers and Integer Programming

We propose a novel Bayesian approach to automated delineation of curvilinear structures that form complex and potentially loopy networks. By representing the image data as a graph of potential paths, we first show how to weight these paths using discriminatively-trained classifiers that are both robust and generic enough to be applied to very different imaging modalities. We then present an Integer Programming approach to finding the optimal subset of paths, subject to structural and topological constraints that eliminate implausible solutions. Unlike earlier approaches that assume a tree topology for the networks, ours explicitly models the fact that the networks may contain loops, and can reconstruct both cyclic and acyclic ones. We demonstrate the effectiveness of our approach on a variety of challenging datasets including aerial images of road networks and micrographs of neural arbors, and show that it outperforms state-of-the-art techniques

On the Relevance of Sparsity for Image Classification

In this paper we empirically analyze the importance of sparsifying representations for classification purposes. We focus on those obtained by convolving images with linear filters, which can be either hand designed or learned, and perform extensive experiments on two important Computer Vision problems, image categorization and pixel classification. To this end, we adopt a simple modular architecture that encompasses many recently proposed models. The key outcome of our investigations is that enforcing sparsity constraints on features extracted in a convolutional architecture does not improve classification performance, whereas it does so when redundancy is artificially introduced. This is very relevant for practical purposes, since it implies that the expensive run-time optimization required to sparsify the representation is not always justified, and therefore that computational costs can be drastically reduced

Vessel Segmentation on Computed Tomography Angiography

International audienceThis short paper describes our contribution in the research aimed at model based vessel segmentation on CTA. Although each partner was involved in a main subject among what follows, the contribution is a joint effort of all the partners, as a result of regular visits in France and Israel, as well as between partners in each country. The French Hospital Partner in Lyon provided a large set of CTA studies, including sets with two studies performed on each patient and about 20 studies suitable for work on other aspects of cardiac vessel segmentation

A NEW INTERACTIVE METHOD FOR CORONARY ARTERIES SEGMENTATION BASED ON TUBULAR ANISOTROPY

In this paper we present a new interactive method for tubular structure extraction. The main application and motivation for this work is vessel tracking in 3D medical images. The basic tools are minimal paths solved using the fast marching algorithm. This leads to interactive tools for the physician by clicking on a small number of points in order to obtain a minimal path between two points or a set of paths in the case of a tree structure. Our method is based on a variant of the minimal path method that models the vessel as a centerline and surface by adding one dimension for the local radius around the centerline. The crucial step of our method is the definition of the local metrics to minimize (based on the local orientation using a Riemannian Metric). This approach is made available for the physician using an Object Oriented Language (OOL) interface. We show results on two CT cardiac images for coronary arteries segmentation. Index Terms — Image segmentation, Image enhancement, Medical diagnosi

Fast Object Segmentation by Growing Minimal Paths from a Single Point on 2D or 3D Images

In this paper, we present a new method for segmenting closed contours and surfaces. Our work builds on a variant of the minimal path approach. First, an initial point on the desired contour is chosen by the user. Next, new keypoints are detected automatically using a front propagation approach. We assume that the desired object has a closed boundary. This a-priori knowledge on the topology is used to devise a relevant criterion for stopping the keypoint detection and front propagation. The final domain visited by the front will yield a band surrounding the object of interest. Linking pairs of neighboring keypoints with minimal paths allows us to extract a closed contour from a 2D image. This approach can also be used for finding an open curve giving extra information as stopping criteria. Detection of a variety of objects on real images is demonstrated. Using a similar idea, we can extract networks of minimal paths from a 3D image called Geodesic Meshing. The proposed method is applied to 3D data with promising results