School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore.
Key Lab for IOT and Information Fusion Technology of Zhejiang, Hangzhou Dianzi University, Hangzhou, China.
PLoS One. 2019 Jun 27;14(6):e0218931. doi: 10.1371/journal.pone.0218931. eCollection 2019.
Endosomes are subcellular organelles which serve as important transport compartments in eukaryotic cells. Fluorescence microscopy is a widely applied technology to study endosomes at the subcellular level. In general, a microscopy image can contain a large number of organelles and endosomes in particular. Detecting and annotating endosomes in fluorescence microscopy images is a critical part in the study of subcellular trafficking processes. Such annotation is usually performed by human inspection, which is time-consuming and prone to inaccuracy if carried out by inexperienced analysts. This paper proposes a two-stage method for automated detection of ring-like endosomes. The method consists of a localization stage cascaded by an identification stage. Given a test microscopy image, the localization stage generates a voting-map by locally comparing the query endosome patches and the test image based on a bag-of-words model. Using the voting-map, a number of candidate patches of endosomes are determined. Subsequently, in the identification stage, a support vector machine (SVM) is trained using the endosome patches and the background pattern patches. Each of the candidate patches is classified by the SVM to rule out those patches of endosome-like background patterns. The performance of the proposed method is evaluated with real microscopy images of human myeloid endothelial cells. It is shown that the proposed method significantly outperforms several state-of-the-art competing methods using multiple performance metrics.
内体是真核细胞中作为重要运输隔室的亚细胞细胞器。荧光显微镜是研究亚细胞水平内体的一种广泛应用的技术。通常，显微镜图像可以包含大量的细胞器，特别是内体。在荧光显微镜图像中检测和注释内体是研究细胞内运输过程的关键部分。这种注释通常由人工检查完成，如果由经验不足的分析师进行，则既耗时又容易出错。本文提出了一种用于自动化检测环状内体的两阶段方法。该方法由一个定位阶段和一个识别阶段级联组成。给定一个测试显微镜图像，定位阶段通过基于词袋模型在查询内体补丁和测试图像之间进行局部比较，生成投票图。使用投票图，可以确定一些候选内体补丁。随后，在识别阶段，使用内体补丁和背景模式补丁来训练支持向量机（SVM）。通过 SVM 对每个候选补丁进行分类，以排除那些类似于内体的背景模式的补丁。使用多种性能指标评估了所提出的方法在真实人类骨髓内皮细胞显微镜图像上的性能。结果表明，该方法在多个性能指标上明显优于几种最先进的竞争方法。
