{'fraction_non_alphanumeric': 0.010655737704918032, 'fraction_numerical': 0.0, 'mean_word_length': 5.82122905027933, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Extraction and Quantification of Actin Cytoskeleton in Microscopic Images Using a Deep Learning Based Framework and a Curve Clustering Model

The actin cytoskeleton plays a fundamental role in biological processes, and its shape continuously changes. Under different stimuli, the actin cytoskeleton can quickly polymerize and dissolve. Quantifying their changes among different time frames is necessary to understand the mechanism of actin filaments. Confocal microscopy makes it possible to image thin filaments, but quantifying the actin cytoskeletal organization’s changes remains challenging. This paper proposes deep learning facilitated pipeline to extract individual filaments from microscopic images. Then we model these extracted filaments as sequences of data points and adopt a curve clustering algorithm to cluster them by their shapes. We use the histogram of curve clusters to represent the actin cytoskeleton organization in each image and quantify the changes by measuring the distances between frequency distributions. Our experiments show that our method achieves better actin filament extraction results and provides an alternative way to reveal the shape-shifting of actin cytoskeleton organizations.