## 4 Discussion  
The current work aimed to propose a semi-automatic and objective system to support the diagnosis and monitoring of chronic wounds and to address the numerous limitations identified in the treatment process of individuals suffering from this pathology. Among  
Figure 11: Final interactive Unity3D workspace displaying the patient’s demographic data and the qualitative and quantitative parameters of the wound. these limitations, the shortage of wound care experts, the lack of standardized protocols, and the difficulties in ensuring continuous monitoring and patient-specific therapy play a crucial role. The first contribution of the work consisted in automatically segmenting the wound from a two-dimensional image through the implementation of a CNN. By training the network on the dataset expanded with additional 319 WV images, an 18% increase in performance on the Test Set was achieved, reaching an average IoU of 66%. In addition to average values, the median IoU was also assessed, scoring 77% for the expanded dataset, indicating most images were recognized with high IoU values, while a few lower-quality images affected the average value. The extraction of the wound contour from two-dimensional images enabled the subsequent implementation of 3D lesion parameter calculation and the visualization of the results in the XR environment. Two-dimensional measurements obtained during the current work aligned with prior studies on ulcer size evaluation [32]. Moreover, further studies such as the one by [PERSON] et al. [33] found that changes in 3D area were significantly larger than 2D area measurements, particularly for larger wounds. They proved that 3D ulcers' bed area monitoring captured changes in ulcer dimensions more accurately compared to 2D images. Thus, integrating depth information for skin ulcer assessment and monitoring can lead to many advantages over traditional 2D methods, providing information about the extent of the internal area of the wound and consequently, an indication of its depth. Previous studies have demonstrated the superiority of a 3D measurement method over the 2D method for calculating wound areas, especially in complex or curved anatomical regions, showing greater precision and reliability in shape and temperature measurements [34; 35; 36]. Along the same lines, the values obtained in this study suggest that they carry informative content also regarding the depth of the wound, as the 3D parameter computation yielded higher values compared to those calculated from 2D images. Another contribution of the current study is the integration of the skin ulcer assessment methodology within an XR environment, while past research focused only on parameter extraction. The proposed tool aimed to support clinicians for enhanced clinical decision-making and patient care, by consulting 2D images and 3D models of the skin ulcer along with quantitative data such as its area and perimeter parameters. Additionally, thermal data indicating infection status and microvascular in the wound and surrounding tissues was overlaid for surface visualization, as it has been shown that the assessment of microvascular aids in predicting wound progression [37]. The obtained Unity3D XR design was reviewed by two dermatology specialists, who emphasized the need for analyzing the ulcer's edge and base separately for accurate assessment and therapeutic insights. Experts valued combining 2D and 3D information and the patient's clinical history to facilitate initial evaluation and enhance and speed up treatment decisions. Despite the promising results, this study has certain limitations that deserve consideration. First, the dataset, which included RGB and their matching depth images, was restricted in size. This limitation may have influenced the 2D segmentation performance, which might be considerably improved with a larger dataset.