## 3 Results
### Semantic vs. Otsu Thresholding Segmentation  
Semantic and Otsu thresholding segmentation were compared in order to verify if the results obtained by AI with the semantic segmentation method were superimposable with those obtained by common thresholding techniques.  
The thresholding segmentation was applied using the open-source Fiji software [24]. As the first step, the Histogram Equalization Fiji plugin was used to enhance contrast in the spatial domain, adjusting the pixel intensity from 0 to 255 [35; 36]. Subsequently, the Otsu segmentation process was chosen as the thresholding segmentation method (Figure 3a,c,e).  
The semantic segmentation was performed with the Dragonfly software [23]. The semantic segmentation allowed us to distinguish not only background from connective tissue signals but also, unlike Otsu segmentation, transversal bundles from longitudinal bundles  
Figure 3: Thresholding (**a,c,e**) vs. semantic (**b,d,f**) segmentation methods. Examples of comparisons on homologous slices: (**a,b**) Crt1_st_wt; (**c,d**) Crt2_lt_no-wt; (**e,f**) Pt2_st_no-wt_l.  
Figure 2: (**a**) The loss function minimization; (**b**) the accuracy trend; training and validation cases were plotted. Only the first 100 epochs were plotted because the network achieved its best performance around this level.  
(Figure 4 and Video S1 in Supplementary Materials); this was achieved with a training based on three classes. However, to compare the semantic with the Otsu thresholding method, in the former, the two classes of collagen bundles (transversal and longitudinal) were grouped and just distinguished from the background (Figure 3b,d,f). The following morphometric parameters were considered: the percentage volume, the anisotropy degree, and the connectivity density. They were represented, in Python, by means \(\pm\) standard deviation (Table 3) of the four volumes extracted from each sample (Figure 5).  
Referring to the volume percentage, in general, the average values obtained by deep learning through semantic segmentation were similar to those obtained by the thresholding segmentation method. Interestingly, the volume percentage distribution throughout the  
Figure 5: Box plots of volume percentage, degree of anisotropy and connectivity density calculated on each volume segmented with deep learning semantic method (red) and Otsu thresholding (blue). For deep learning, both longitudinal and transversal fibers were considered together for comparison.  
whole sample of Ctr2_lt_no-wt was significantly wider using semantic segmentation than in the case of the Otsu thresholding method.  
The degree of anisotropy trend was found to be similar using the thresholding or the semantic segmentation techniques.  
Conversely, referring to the connectivity density, the average values obtained by deep learning through semantic segmentation were significantly higher than those obtained by the Otsu thresholding segmentation method in all of the samples except for the Ctr1_st_wt. This higher level of connectivity was probably due to a better discrimination of the single fibers; consequently, each intertwinement was better recognized.  
Interestingly, using semantic segmentation, the connectivity density was significantly higher in Pq2_st_no-wt_l than in all of the other samples. This was most likely attributable to the immaturity of the connective tissue present in this biopsy, also demonstrated by the morphological evidence shown in Figure S1 of the Supplementary Materials. In fact, the abundant presence of micro vessels (Figure S1a) and the presence of collagen fibers still strongly disorganized (Figure S1b) at a three-dimensional level suggested the presence of a connective tissue only at the beginning of the remodeling phase, with a wound not yet completely healed at the connective level.