Localizing and extracting filament distributions from microscopy images

The filament sensor for near real-time detection of cytoskeletal fiber structures

A reliable extraction of filament data from microscopic images is of high interest in the analysis of acto-myosin structures as early morphological markers in mechanically guided differentiation of human mesenchymal stem cells and the understanding of the underlying fiber arrangement processes. In this paper, we propose the filament sensor (FS), a fast and robust processing sequence which detects and records location, orientation, length, and width for each single filament of an image, and thus allows for the above described analysis. The extraction of these features has previously not been possible with existing methods. We evaluate the performance of the proposed FS in terms of accuracy and speed in comparison to three existing methods with respect to their limited output. Further, we provide a benchmark dataset of real cell images along with filaments manually marked by a human expert as well as simulated benchmark images. The FS clearly outperforms existing methods in terms of computational runtime and filament extraction accuracy. The implementation of the FS and the benchmark database are available as open source.

Extraction of Individual Filaments from 2D Confocal Microscopy Images of Flat Cells

A crucial step in understanding the architecture of cells and tissues from microscopy images, and consequently explain important biological events such as wound healing and cancer metastases, is the complete extraction and enumeration of individual filaments from the cellular cytoskeletal network. Current efforts at quantitative estimation of filament length distribution, architecture and orientation from microscopy images are predominantly limited to visual estimation and indirect experimental inference. Here we demonstrate the application of a new algorithm to reliably estimate centerlines of biological filament bundles and extract individual filaments from the centerlines by systematically disambiguating filament intersections. We utilize a filament enhancement step followed by reverse diffusion based filament localization and an integer programming based set combination to systematically extract accurate filaments automatically from microscopy images. Experiments on simulated and real confocal microscope images of flat cells (2D images) show efficacy of the new method.

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.

Quantitative analysis of F-actin redistribution in astrocytoma cells treated with candidate pharmaceuticals

Actin fibers (F-actin) control the shape and internal organization of cells, and generate force. It has been long appreciated that these functions are tightly coupled, and in some cases drive cell behavior and cell fate. The distribution and dynamics of F-actin is different in cancer versus normal cells and in response to small molecules, including actin-targeting natural products and anticancer drugs. Therefore, quantifying actin structural changes from high resolution fluorescence micrographs is necessary for further understanding actin cytoskeleton dynamics and phenotypic consequences of drug interactions on cells. We applied an artificial neural network algorithm, which used image intensity and anisotropy measurements, to quantitatively classify F-actin subcellular features into actin along the edges of cells, actin at the protrusions of cells, internal fibers and punctate signals. The algorithm measured significant increase in F-actin at cell edges with concomitant decrease in internal punctate actin in astrocytoma cells lacking functional neurofibromin and p53 when treated with three structurally-distinct anticancer small molecules: OSW1, Schweinfurthin A (SA) and a synthetic marine compound 23'-dehydroxycephalostatin 1. Distinctly different changes were measured in cells treated with the actin inhibitor cytochalasin B. These measurements support published reports that SA acts on F-actin in NF1(-/-) neurofibromin deficient cancer cells through changes in Rho signaling. Quantitative pattern analysis of cells has wide applications for understanding mechanisms of small molecules, because many anti-cancer drugs directly or indirectly target cytoskeletal proteins. Furthermore, quantitative information about the actin cytoskeleton may make it possible to further understand cell fate decisions using mathematically testable models.

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.