## 4 Discussion  
This paper presents AIM, a computational tool for the automatic quantification of the results of scratch wound healing assays. Besides the technical description of this method, its equivalence to commonly-used techniques for the analysis of wound images and the quantification of the extension of the scratch was shown. This comparison also allowed evaluating the advantages and disadvantages of each method and the conditions required to obtain reliable results. In particular, the manual method, which consists in approximating the wound with a rectangle, was shown to be very time consuming, with an average analysis time of approximately 35 s/image. This characteristic, combined with the high dependence on the operator, makes this strategy potentially unreliable when a large number of images must be analyzed. Indeed, obtaining accurate results with this technique requires the cell front to be vertical and the user to be consistent when drawing the rectangle. This generally involves setting the width of the wound as the best fit in a particular point (e.g., the center of the image) that is maintained fixed throughout the analysis. Another common analysis method for scratch wound healing assays involves the segmentation of the images through an ImageJ macro. While being less dependent on the user, this strategy was shown to be prone to generating a large number of false positives that led to a tendency of overestimating the wound area and to a higher percentage difference from the ground truth, when compared to the other methods (Figure 3). Furthermore, this sensitivity requires a careful planning of the experiment, which must include an adequate number of time points, but avoid images where the wound edges are highly irregular, as in Figure 1 at T = 48. Furthermore, different microscopy setups might be associated with the necessity of adjusting the segmentation procedure and thus with the specific knowledge of image processing. A third analysis strategy considered during AIM's validation was Tscratch, software for the semi-automatic analysis of scratch wound healing assays. It demonstrated a high accuracy and a good precision in all the tested conditions, despite showing a mild dependence on the structure of the time course, associated with some outliers in the distribution of the differential invasiveness (Figure 5). The average analysis time for this method was about 1 s/image, and it was mostly independent of the user. Its installation, however, required MATLAB and specific programming knowledge, characteristics that might limit its usability and represent the cause of its scarce diffusion and usage in cancer research. For some of the images considered in this analysis, the ground truth was determined through the manual tracing of the wound's edges. This method, while capable of accurately identifying the scratch and determining its area, is extremely time consuming, with an average analysis time of 120 s/image and highly dependent on the user's ability of consistently elaborating all the images. Therefore, it cannot be considered as a feasible alternative for the quantification of the results of scratch wound healing assays. Here, we have presented AIM, which to the best of our knowledge, is the first fully-automatic software for the segmentation and the quantification of cell invasiveness in a 2D framework. Beside showing its equivalence to the most commonly-used strategies for wound area quantification, AIM was demonstrated to be fast, with an average analysis time below 1.5 s/image, and able to accurately evaluate differential invasiveness independently of the structure of the time course (Figure 5). These characteristics make the presented tool very versatile and suitable for the study of a wide range of experimental conditions. Furthermore, AIM is fully automated, and thus, its results are independent of the operator and completely objective.