a new method to address unmet needs for extracting individual cell migration features from a large number of cells embedded in 3d volumes一种新方法来解决未满足的需求中提取单个细胞的迁移特性从大量的细胞嵌入在3 d卷.pdfVIP

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a new method to address unmet needs for extracting individual cell migration features from a large number of cells embedded in 3d volumes一种新方法来解决未满足的需求中提取单个细胞的迁移特性从大量的细胞嵌入在3 d卷.pdf

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a new method to address unmet needs for extracting individual cell migration features from a large number of cells embedded in 3d volumes一种新方法来解决未满足的需求中提取单个细胞的迁移特性从大量的细胞嵌入在3 d卷

A New Method to Address Unmet Needs for Extracting Individual Cell Migration Features from a Large Number of Cells Embedded in 3D Volumes 1 ´ 2 1 1,2 Ivan Adanja , Veronique Megalizzi , Olivier Debeir , Christine Decaestecker * ´ 1 Laboratory of Image Synthesis and Analysis (LISA), Faculty of Applied Science, Universite Libre de Bruxelles (U.L.B.), Brussels, Belgium, 2 Laboratory of Toxicology, Faculty ´ of Pharmacy, Universite Libre de Bruxelles (U.L.B.), Brussels, Belgium Abstract Background: In vitro cell observation has been widely used by biologists and pharmacologists for screening molecule- induced effects on cancer cells. Computer-assisted time-lapse microscopy enables automated live cell imaging in vitro, enabling cell behavior characterization through image analysis, in particular regarding cell migration. In this context, 3D cell assays in transparent matrix gels have been developed to provide more realistic in vitro 3D environments for monitoring cell migration (fundamentally different from cell motility behavior observed in 2D), which is related to the spread of cancer and metastases. Methodology/Principal Findings: In this paper we propose an improved automated tracking method that is designed to robustly and individually follow a large number of unlabeled cells observed under phase-contrast microscopy in 3D gels. The method automatically detects and tracks individual cells across a sequence of acquired volumes, using a template matching filtering method that in turn allows for robust detection and mean-shift tracking. The robustness of the met