The Filament Sensor for Near Real-Time Detection of Cytoskeletal Fiber Structures

TL;DR

The paper introduces the filament sensor (FS), a fast and accurate method for extracting detailed filament features from microscopic images, enabling advanced analysis of cytoskeletal structures in biological research.

Contribution

The filament sensor (FS) is a novel, robust, and efficient method that extracts location, orientation, length, and width of filaments, outperforming existing methods in speed and accuracy.

Findings

FS outperforms existing methods in accuracy

FS is faster than previous approaches

Open source implementation and benchmark dataset provided

Abstract

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.