A novel method for evaluating and visualizing scratch wound healing assays using level-set and image sector analysis

Scratch wound healing assays are widely used to study collective cell migration, essential for understanding tissue regeneration, drug effects, and wound healing mechanisms. However, conventional analyses often rely on wound edge dynamics or individual cell tracks, limiting spatial insight into migration behavior. We present a sector-based analytical framework that reinterprets time-lapse microscopy data by dividing each image into defined spatial regions across the field of view. This enables spatially resolved characterization of how cell populations migrate over time. To address challenges of low contrast and uneven illumination in bright-field microscopy, we apply a level-set segmentation algorithm that robustly detects the wound edge. Using this approach, we show that both cell velocity and trajectory vary with distance from the wound boundary. We introduce a novel metric, the sector-boundary distance, to identify regions where cells migrate faster along nonradial paths. To assess chemotactic activation, cells were treated with the chemokine CXCL10 to stimulate motility via CXCR3-mediated signaling. Statistical testing showed that, in treated cells, the proportion of highly motile cells was significantly associated with wound closure, even in regions distant from the scratch, whereas directionality played a limited role. By contrast, untreated cells exhibited weaker and less organized migration patterns. These findings highlight how local cellular activity contributes to healing in a treatment-dependent manner. Our method bridges global wound-level analysis and local cell-scale behavior by combining single-cell tracking with precise boundary detection. The complete framework is available as open-source software, including a user-friendly web application that enables interactive analysis of microscopy data.