Open Active Contours Research Articles

Towards real time guide wire shape extraction in fluoroscopic sequences: A two phase deep learning scheme to extract sparse curvilinear structures.

Real time localization and shape extraction of guide wire in fluoroscopic images plays a significant role in the image guided navigation during cerebral and cardiovascular interventions. Given the complexity of the non-rigid and sparse characteristics of guide wire structures, and the low SNR(Signal Noise Ratio) of fluoroscopic images, traditional handcrafted guide wire tracking methods such as Frangi filter, Hessian Matrix, or open active contour usually produce insufficient accuracy with high computational cost, and may require extra human intervention for proper initialization or correction. The application of deep learning techniques to guide wire tracking is reported to produce significant improvement in guide wire localization accuracy, but the heavy calculation cost is still a concern. In this paper we propose a two phase deep learning scheme for accurate and real time guide wire shape extraction in fluoroscopic sequences. In the first phase we train a guide wire localization network to pick image regions containing guide wire structures. From the picked image regions, we train a guide wire shape extraction network in the second phase to mark the guide wire pixels. We report that our proposed method can accurately distinguish about 99% of the guide wire structure pixels, and the falsely detected pixels in the background are close to 0, the average offset from the ground truth is less than 1 pixel. For extreme cases where traditional handcrafted method may fail, our proposed method can still extract guide wire completely and accurately. The processing time for a 512 × 512 pixels image is 78 ms. Compared with the traditional filtering based method from our previous work, we show that our proposed method can achieve more accurate and stable performance. Compared with other deep learning methods, our proposed method significantly improve calculation efficiency to meet the real time requirement of clinical applications.

Toward an automatic tool for oligoclonal band detection in cerebrospinal fluid and tears for multiple sclerosis diagnosis: lane segmentation based on a ribbon univariate open active contour.

The latest revision of multiple sclerosis diagnosis guidelines emphasizes the role of oligoclonal band detection in isoelectric focusing images of cerebrospinal fluid. Recent studies suggest tears as a promising noninvasive alternative to cerebrospinal fluid. We are developing the first automatic method for isoelectric focusing image analysis and oligoclonal band detection in cerebrospinal fluid and tear samples. The automatic analysis would provide an accurate, fast analysis and would reduce the expert-dependent variability and errors of the current visual analysis. In this paper, we describe a new effective model for the fully automated segmentation of highly distorted lanes in isoelectric focusing images. This approach is a new formulation of the classic parametric active contour problem, in which an open active contour is constrained to move from the top to the bottom of the image, and the x-axis coordinate is expressed as a function of the y-axis coordinate. The left and right edges of the lane evolved together in a ribbon-like shape so that the full width of the lane was captured reliably. The segmentation algorithm was implemented using a multiresolution approach in which the scale factor and the active contour control points were progressively increased. The lane segmentation algorithm was tested on a database of 51 isoelectric focusing images containing 419 analyzable lanes. The new model gave robust results for highly curved lanes, weak edges, and low-contrast lanes. A total of 98.8% of the lanes were perfectly segmented, and the remaining 1.2% had only minor errors. The computation time (1s per membrane) is negligible. This method precisely defines the region of interest in each lane and thus is a major step toward the first fully automatic tool for oligoclonal band detection in isoelectric focusing images. Graphical abstract.

Automated Tracking of Biopolymer Growth and Network Deformation with TSOAX

Studies of how individual semi-flexible biopolymers and their network assemblies change over time reveal dynamical and mechanical properties important to the understanding of their function in tissues and living cells. Automatic tracking of biopolymer networks from fluorescence microscopy time-lapse sequences facilitates such quantitative studies. We present an open source software tool that combines a global and local correspondence algorithm to track biopolymer networks in 2D and 3D, using stretching open active contours. We demonstrate its application in fully automated tracking of elongating and intersecting actin filaments, detection of loop formation and constriction of tilted contractile rings in live cells, and tracking of network deformation under shear deformation.

Active Canny: edge detection and recovery with open active contour models

The authors introduce an edge detection and recovery framework based on open active contour models (snakelets) to mitigate the problem of noisy or broken edges produced by classical edge detection algorithms, like Canny. The idea is to utilise the local continuity and smoothness cues provided by strong edges and grow them to recover the missing edges. This way, the strong edges are used to recover weak or missing edges by considering the local edge structures, instead of blindly linking edge pixels based on a threshold. The authors initialise short snakelets on the gradient magnitudes or binary edges automatically and then deform and grow them under the influence of gradient vector flow. The output snakelets are able to recover most of the breaks or weak edges and provide a smooth edge representation of the image; they can also be used for higher-level analysis, like contour segmentation.

Revealing and Quantifying the Three-Dimensional Nano- and Microscale Structures in Self-Assembled Cellulose Microfibrils in Dispersions.

Cellulose microfibrils (CMFs) are an important nanoscale building block in many novel biobased functional materials. The spatial nano- and microscale organization of the CMFs is a crucial factor for defining the properties of these materials. Here, we report for the first time a direct three-dimensional (3D) real-space analysis of individual CMFs and their networks formed after ultrahigh-shear-induced transient deagglomeration and self-assembly in a solvent. Using point-scanning confocal microscopy combined with tracking the centerlines of the fibrils and their junctions by a stretching open active contours method, we reveal that dispersions of the native CMFs assemble into highly heterogeneous networks of individual fibrils and bundles. The average network mesh size decreases with increasing CMF volume fraction. The cross-sectional width and the average length between the twists in the ribbon-shaped CMFs are directly determined and compared well with that of fibrils in the dried state. Finally, the generality of the fluorescent labeling and imaging approach on other CMF sources is illustrated. The unique ability to quantify in situ the multiscale structure in CMF dispersions provides a powerful tool for the correlation of process–structure–property relationship in cellulose-containing composites and dispersions.

Automatic detection and morphological delineation of bacteriophages in electron microscopy images

Automatic detection, recognition and geometric characterization of bacteriophages in electron microscopy images was the main objective of this work. A novel technique, combining phase congruency-based image enhancement, Hough transform-, Radon transform- and open active contours with free boundary conditions-based object detection was developed to detect and recognize the bacteriophages associated with infection and lysis of cyanobacteria Aphanizomenon flos-aquae. A random forest classifier designed to recognize phage capsids provided higher than 99% accuracy, while measurable phage tails were detected and associated with a correct capsid with 81.35% accuracy. Automatically derived morphometric measurements of phage capsids and tails exhibited lower variability than the ones obtained manually. The technique allows performing precise and accurate quantitative (e.g. abundance estimation) and qualitative (e.g. diversity and capsid size) measurements for studying the interactions between host population and different phages that infect the same host.

Topology adaptive vessel network skeleton extraction with novel medialness measuring function

Vessel tree skeleton extraction is widely applied in vascular structure segmentation, however, conventional approaches often suffer from the adjacent interferences and poor topological adaptability. To avoid these problems, a robust, topology adaptive tree-like structure skeleton extraction framework is proposed in this paper. Specifically, to avoid the adjacent interferences, a local message passing procedure called Gaussian affinity voting (GAV) is proposed to realize adaptive scale-growing of vessel voxels. Then the medialness measuring function (MMF) based on GAV, namely GAV–MMF, is constructed to extract medialness patterns robustly. In order to improve topological adaptability, a level-set graph embedded with GAV–MMF is employed to build initial curve skeletons without any user interaction. Furthermore, the GAV–MMF is embedded in stretching open active contours (SOAC) to drive the initial curves to the expected location, maintaining smoothness and continuity. In addition, to provide an accurate and smooth final skeleton tree topology, topological checks and skeleton network reconfiguration is proposed. The continuity and scalability of this method is validated experimentally on synthetic and clinical images for multi-scale vessels. Experimental results show that the proposed method achieves acceptable topological adaptability for skeleton extraction of vessel trees.

SOAX: a software for quantification of 3D biopolymer networks.

Filamentous biopolymer networks in cells and tissues are routinely imaged by confocal microscopy. Image analysis methods enable quantitative study of the properties of these curvilinear networks. However, software tools to quantify the geometry and topology of these often dense 3D networks and to localize network junctions are scarce. To fill this gap, we developed a new software tool called “SOAX”, which can accurately extract the centerlines of 3D biopolymer networks and identify network junctions using Stretching Open Active Contours (SOACs). It provides an open-source, user-friendly platform for network centerline extraction, 2D/3D visualization, manual editing and quantitative analysis. We propose a method to quantify the performance of SOAX, which helps determine the optimal extraction parameter values. We quantify several different types of biopolymer networks to demonstrate SOAX's potential to help answer key questions in cell biology and biophysics from a quantitative viewpoint.

Prostate boundary segment extraction using cascaded shape regression and optimal surface detection.

In this paper, we proposed a new method (CSR+OSD) for the extraction of irregular open prostate boundaries in noisy extracorporeal ultrasound image. First, cascaded shape regression (CSR) is used to locate the position of prostate boundary in the images. In CSR, a sequence of random fern predictors are trained in a boosted regression manner, using shape-indexed features to achieve invariance against position variations of prostate boundaries. Afterwards, we adopt optimal surface detection (OSD) to refine the prostate boundary segments across 3D sections globally and efficiently. The proposed method is tested on 162 ECUS images acquired from 8 patients with benign prostate hyperplasia. The method yields a Root Mean Square Distance of 2.11±1.72 mm and a Mean Absolute Distance of 1.61±1.26 mm, which are lower than those of JFilament, an open active contour algorithm and Chan-Vese region based level set model, respectively.

Lane Detection and Tracking Using a Parallel-snake Approach

In this paper, we discuss the design of a parallel-snake model for lane detection and the use of a Kalman filter for tracking. The parallel-snake model is an extension of the open active contour model through the application of a parallel constraint to two open snakes. Compared with other models, this model can handle lanes with broken boundaries and reduce the convergence time with the aid of the parallel constraint and a double external energy force from two parallel snakes. To solve the problem in previous snake models, whereby the external force is lost on images with a low gradient, a balloon force is utilized to expand the double snakes from the center of the road to the lane boundaries. Because lane boundaries do not retain the parallel property, the captured images are transformed into a bird's-eye view to retrieve the parallel property of lane boundaries by planar homography. At least four corresponding points are determined and the EM-based vanishing point estimation algorithm is applied to these points to estimate the planar homography. Finally, we use a Kalman filter for parameter optimization in lane tracking considering the continuity of lane parameters between consecutive frames; i.e., to predict the parameters of subsequent frames from the previous frame and refine the estimated results to improve robustness. Experimental results show that the proposed method achieves good performance on lane datasets with shadows, variations in illumination, and broken boundaries. Furthermore, it can handle both structured and unstructured (country) roads well.

Label-free evaluation of angiogenic sprouting in microengineered devices using ultrahigh-resolution optical coherence microscopy

Understanding the mechanism of angiogenesis could help to decipher wound healing and embryonic development and to develop better treatment for diseases such as cancer. Microengineered devices were developed to reveal the mechanisms of angiogenesis, but monitoring the angiogenic process nondestructively in these devices is a challenge. In this study, we utilized a label-free imaging technique, ultrahigh-resolution optical coherence microscopy (OCM), to evaluate angiogenic sprouting in a microengineered device. The OCM system was capable of providing ∼1.5-μm axial resolution and ∼2.3-μm transverse resolution. Three-dimensional (3-D) distribution of the sprouting vessels in the microengineered device was imaged over 0.6×0.6×0.5 mm3, and details such as vessel lumens and branching points were clearly visualized. An algorithm based on stretching open active contours was developed for tracking and segmenting the sprouting vessels in 3-D-OCM images. The lengths for the first-, second-, and third-order vessels were measured as 127.8±48.8 μm (n=8), 67.3±25.9 μm (n=9), and 62.5±34.7 μm (n=10), respectively. The outer diameters for the first-, second-, and third-order vessels were 13.2±1.0, 8.0±2.1, and 4.4±0.8 μm, respectively. These results demonstrate OCM as a promising tool for nondestructive and label-free evaluation of angiogenic sprouting in microengineered devices.

Localisation of Drosophila embryos using active contours in channel spaces.

In this paper, we introduce an active contour based scheme to localise Drosophila embryos in RGB images. An active contour (initiated as a closed one) maybe converge to an open contour, e.g., in the case that a targeting embryo is touched by a neighbouring one. We propose an algorithmic strategy to detect and restore open active contours. The experiment results show the promise of the proposed scheme.

3D actin network centerline extraction with multiple active contours

Fluorescence microscopy is frequently used to study two and three dimensional network structures formed by cytoskeletal polymer fibers such as actin filaments and actin cables. While these cytoskeletal structures are often dilute enough to allow imaging of individual filaments or bundles of them, quantitative analysis of these images is challenging. To facilitate quantitative, reproducible and objective analysis of the image data, we propose a semi-automated method to extract actin networks and retrieve their topology in 3D. Our method uses multiple Stretching Open Active Contours (SOACs) that are automatically initialized at image intensity ridges and then evolve along the centerlines of filaments in the network. SOACs can merge, stop at junctions, and reconfigure with others to allow smooth crossing at junctions of filaments. The proposed approach is generally applicable to images of curvilinear networks with low SNR. We demonstrate its potential by extracting the centerlines of synthetic meshwork images, actin networks in 2D Total Internal Reflection Fluorescence Microscopy images, and 3D actin cable meshworks of live fission yeast cells imaged by spinning disk confocal microscopy. Quantitative evaluation of the method using synthetic images shows that for images with SNR above 5.0, the average vertex error measured by the distance between our result and ground truth is 1 voxel, and the average Hausdorff distance is below 10 voxels.

Efficient Multiple Object Tracking Using Mutually Repulsive Active Membranes

Studies of social and group behavior in interacting organisms require high-throughput analysis of the motion of a large number of individual subjects. Computer vision techniques offer solutions to specific tracking problems, and allow automated and efficient tracking with minimal human intervention. In this work, we adopt the open active contour model to track the trajectories of moving objects at high density. We add repulsive interactions between open contours to the original model, treat the trajectories as an extrusion in the temporal dimension, and show applications to two tracking problems. The walking behavior of Drosophila is studied at different population density and gender composition. We demonstrate that individual male flies have distinct walking signatures, and that the social interaction between flies in a mixed gender arena is gender specific. We also apply our model to studies of trajectories of gliding Myxococcus xanthus bacteria at high density. We examine the individual gliding behavioral statistics in terms of the gliding speed distribution. Using these two examples at very distinctive spatial scales, we illustrate the use of our algorithm on tracking both short rigid bodies (Drosophila) and long flexible objects (Myxococcus xanthus). Our repulsive active membrane model reaches error rates better than per fly per second for Drosophila tracking and comparable results for Myxococcus xanthus.

EXTRACTION AND ANALYSIS OF ACTIN NETWORKS BASED ON OPEN ACTIVE CONTOUR MODELS.

Network structures formed by actin filaments are present in many kinds of fluorescence microscopy images. In order to quantify the conformations and dynamics of such actin filaments, we propose a fully automated method to extract actin networks from images and analyze network topology. The method handles well intersecting filaments and, to some extent, overlapping filaments. First we automatically initialize a large number of Stretching Open Active Contours (SOACs) from ridge points detected by searching for plus-to-minus sign changes in the gradient map of the image. These initial SOACs then elongate simultaneously along the bright center-lines of filaments by minimizing an energy function. During their evolution, they may merge or stop growing, thus forming a network that represents the topology of the filament ensemble. We further detect junction points in the network and break the SOACs at junctions to obtain "SOAC segments". These segments are then re-grouped using a graph-cut spectral clustering method to represent the configuration of actin filaments. The proposed approach is generally applicable to extracting intersecting curvilinear structures in noisy images. We demonstrate its potential using two kinds of data: (1) actin filaments imaged by Total Internal Reflection Fluorescence Microscopy (TIRFM) in vitro; (2) actin cytoskeleton networks in fission yeast imaged by spinning disk confocal microscopy.

Segmentation and tracking of cytoskeletal filaments using open active contours

We use open active contours to quantify cytoskeletal structures imaged by fluorescence microscopy in two and three dimensions. We developed an interactive software tool for segmentation, tracking, and visualization of individual fibers. Open active contours are parametric curves that deform to minimize the sum of an external energy derived from the image and an internal bending and stretching energy. The external energy generates (i) forces that attract the contour toward the central bright line of a filament in the image, and (ii) forces that stretch the active contour toward the ends of bright ridges. Images of simulated semiflexible polymers with known bending and torsional rigidity are analyzed to validate the method. We apply our methods to quantify the conformations and dynamics of actin in two examples: actin filaments imaged by TIRF microscopy in vitro, and actin cables in fission yeast imaged by spinning disk confocal microscopy.