## 2 Materials and Methods
### Aim  
AIM is our freeware software tool for the automatic elaboration of the scratch wound healing assay. It is developed in MATLAB R2016a, using the image processing toolbox, and is available at [[http://www.mcbeng.it/en/category/software.html](http://www.mcbeng.it/en/category/software.html)]([http://www.mcbeng.it/en/category/software.html](http://www.mcbeng.it/en/category/software.html)), both as source code and a standalone application (released under the GNU Public License, GPL v2). Unlike the previously-described methods, AIM relies on the computation of the local entropy to identify the wound. This parameter quantifies the amount of information coded within the neighborhood of the current pixel, which can be computed applying Equation (1), where \(M\) is the total number of gray levels (256 for 8-bit images) and \(p_{k}\) is the probability of gray level \(k\). \[H=-\sum_{k=0}^{M-1}p_{k}\text{log}_{2}(p_{k}) \tag{1}\]  
Indeed, the wound, due to its homogeneity, will be associated with a lower amount of information with respect to the regions containing the cells, thus providing an unambiguous criterion to identify the region of interest. In AIM, a square neighborhood of a side of 19 pixels is considered, and the entropy calculation is implemented as a mex file in C language, for improved performances. The computation time is further reduced by the scaling of the original image to the format that is closer to 600 \(\times\) 800 pixels and maintains the proportion between width and height. This operation was demonstrated to have a negligible effect on the results. The successive step of the elaboration consists of applying a thresholding procedure ([PERSON]'s method [19]) on the local entropy image, to identify the regions devoid of cells. A black and white morphological closing, using a rectangular structuring element of extension equal to 1/80 of that of the image, completes the segmentation procedure. The areas of the cell-free regions are then clustered into two groups, through a thresholding procedure applied to their histogram [19], and the one with the largest average extension is identified as the wound. This strategy maintains its efficacy even when a number of regions must be identified, due to the fact that some cells have already bridged the gap between the two fronts. Finally, the three outputs of AIM are produced. The first one consists of a text file in which the extension of the wound segmented in each image is reported, together with the uniformity index. This value, computed as in Equation (2), provides an indication of the variability of the initial wound area for each condition. \[UI=100\frac{max(\Delta Area)}{mean(Area)} \tag{2}\]  
This document is saved in a folder, named \"Experiment_name_Results/\", automatically created by the software in the same path as the dataset. In this case, Experiment_name is the name of the parent folder of the experiment. Another output of AIM is a graphical representation, for the different conditions tested, of the change in average wound area over time. To eliminate the dependence on the initial wound extension, every wound is normalized with respect to the corresponding initial value. Finally, a segmented version of all the images considered in the analysis is saved in the subdirectory of \"Experiment_name_Results/\" named \"images\". These.png files allow the user to verify the software's results, as the border of the region recognized as the wound is highlighted in red. To improve its usability, AIM includes a simple graphical user interface (GUI) that allows one to easily select the images to be analyzed and start their segmentation. In order to ensure that the software correctly elaborates the images and reports the results, it is important to organize the data according to a specific structure. In particular, the parent folder, which identifies the experiment, must contain one folder for every tested condition (i.e., control and TGF-\(\beta\)1).