This data set was taken from the TScratch package, with many images that contain scattered cells. The region-classification is designed to deal with this problem. When considering the final segmentation, MultiCellSeg significantly tops the alternative in all data sets. In principle, the second phase of MultiCellSeg may be plugged in to enhance the performance of Scratch’s second phase, but TScratch seems to be less sensitive to small details, which results in significantly less fine regions then with our approach. Utilization of several types of features on several scales makes MultiCellSeg robust for varying conditions. In contrast to other approaches that tend to refrain from fine details to avoid gross mistakes or use data-specific assumptions, our algorithm operates in higher spatial resolution, detects small regions of interest and then decides whether to keep or to discard them via post processing, in a fully automated manner. As a result, in many images where the wound is almost healed, our algorithm performs satisfactorily, whereas other algorithms fail to mark open regions, as exemplified in Fig. 4. To further enhance the proposed segmentation performance, one can suit a model to fit a specific experiment, cell type or imaging conditions. This can be exceedingly useful nowadays, when high-throughput experiments are performed, each with hundreds of images. To this end, one image should be manually marked to apply the training phase in our algorithm. This process is only partly automatic, but it requires noparameter setting and may result in notable improvement in performance with minimal effort. The automatic, accurate zero-parameters MultiCellSeg may serve as a tool for various biological analyses. MultiCellSeg’s Matlab source code is freely available as standalone software to allow others to use it for wound healing analyses, multi-cellular bright field cells segmentation, and for other applications yet to evolve. Wound healing assay is common and is applied by many research groups, but its analysis is very narrow in the sense that only a few measures are considered: the healing rate is calculated over a short period of time. The VE-822 approach presented here can become the cornerstone for novel methods to be exploited in wound healing analysis. To analyze large data sets such as frequently sampled wound healing assays, we suggest to perform manual marking of a few images to train a classifier that will be used to segment the entire time-lapse experiment. Producing these high-temporal-resolution progress graphs may reveal biological processes that are currently unknown, such as the linearity of the healing process, as described here. Another potential corollary is to model the motion patterns of cells throughout the healing process. This is an open question of current interest. Modeling cellular motility patterns under stimulants/inhibitors treatments may facilitate the understanding of cell motility mechanisms and enable the development of new anti-metastatic drugs.