Liu Yi, Nedo Alexander, Seward Kody, Caplan Jeffrey, Kambhamettu Chandra
University of Delaware, Newark, DE, USA.
Proc Int Conf Image Proc. 2020 Oct;2020:2506-2510. doi: 10.1109/ICIP40778.2020.9191337. Epub 2020 Sep 30.
The actin filament plays a fundamental role in numerous cellular processes such as cell growth, proliferation, migration, division, and locomotion. The actin cytoskeleton is highly dynamical and can polymerize and depolymerize in a very short time under different stimuli. To study the mechanics of actin filament, quantifying the length and number of actin filaments in each time frame of microscopic images is fundamental. In this paper, we adopt a Convolutional Neural Network (CNN) to segment actin filaments first, and then we utilize a modified Resnet to detect junctions and endpoints of filaments. With binary segmentation and detected keypoints, we apply a fast marching algorithm to obtain the number and length of each actin filament in microscopic images. We have also collected a dataset of 10 microscopic images of actin filaments to test our method. Our experiments show that our approach outperforms other existing approaches tackling this problem regarding both accuracy and inference time.
肌动蛋白丝在众多细胞过程中发挥着基础性作用，如细胞生长、增殖、迁移、分裂和运动。肌动蛋白细胞骨架具有高度动态性，在不同刺激下能在极短时间内聚合和解聚。为了研究肌动蛋白丝的力学特性，在显微镜图像的每个时间帧中量化肌动蛋白丝的长度和数量至关重要。在本文中，我们首先采用卷积神经网络（CNN）对肌动蛋白丝进行分割，然后利用改进的残差网络（Resnet）检测丝的连接点和端点。通过二值分割和检测到的关键点，我们应用快速行进算法来获取显微镜图像中每条肌动蛋白丝的数量和长度。我们还收集了一个包含10张肌动蛋白丝显微镜图像的数据集来测试我们的方法。我们的实验表明，在准确性和推理时间方面，我们的方法优于其他解决该问题的现有方法。
