## 4 Discussion
Preliminary results indicated that this method shows promise for accurately and automatically determining the size and extent of BCC lesions [26]. Our study highlights in the material and methods section the challenges and the immense amount of time required to accurately reconstruct complex 3D vascular networksmanually. Automations, such as the segmentation methods presented by [PERSON] et al. could address some of these limitations, especially in terms of scalability and clinical applicability. The integration of automatic segmentation tools, as proposed by [PERSON] et al., could significantly reduce processing time and improve the consistency of datasets in the future. However, upstream manual tracking is still a proven approach for detailed 3D reconstructions. The authors provided parafoveal OCT angiography images of the retina with associated manual segmentations as a reference. Various advanced methods for vessel enhancement and binarization, including both hand-crafted filters and neural network architectures, were investigated. The results showed that deep learning architectures such as U-Net and CS-Net achieved the best performance. Furthermore, the choice of segmentation method was found to have a significant impact on the accuracy of clinically relevant metrics, such as vessel density. The authors published their dataset and source code to support standardization efforts in OCTA image segmentation [27]. However, this work was done with OCTA for the eyes, with LC-OCT in skin just a few studies are available, in which also a U-Net was used for AI segmentation of skin layers as in healthy skin or AI [17; 28; 29; 30; 31; 32; 33; 34; 35]. [PERSON] et. al. also showed that although manual tracing of vessels provides highly detailed reconstructions, it is time-consuming and prone to subjective errors. Automated segmentation algorithms, such as the U-Net architectures described by the authors, could overcome these limitations and at the same time ensure greater reproducibility [16]. In their publication, [PERSON] et al. described the three-dimensional modeling and analysis of retinal microvasculature, a major advance in ophthalmic imaging. They developed a precise approach for 3D reconstruction of the retinal vasculature based on high-resolution OCT angiography images. This voxel-based algorithm enables a detailed visualization of the microvasculature, including geometric parameters such as curvature, bifurcations, and diameter. The metrics presented, including the tortuosity index for quantifying vessel tortuosity and metrics for branching complexity and connectivity, provide new insights into the spatial morphology of vessels. [PERSON] et al. showed that this method is particularly useful for the early detection of vascular abnormalities in the eye and for monitoring changes during therapeutic interventions. The authors also mentioned the integration of machine learning to further improve the automation and accuracy of the method [36]. This is a goal that we are also pursuing with this preliminary work. Although manual segmentation of vessels provides precise results, it is susceptible to operator bias as it depends on the subjective interpretation and manual tracing of the operator. Despite training and random checks, complete objectivity cannot be guaranteed. Future approaches using automated or AI-based methods could overcome these limitations, reducing manual effort and at the same time enabling greater consistency and reproducibility of results. In particular, for (skin) tumors, which are known to exhibit pronounced polymorphisms in dermatoscopic evaluations, the spatial component can now be visualized with our approach in 3D. This allows for the analysis of the spatial distribution and structural characteristics of blood vessels within the tumor microenvironment. Fundamentally, these findings provide critical insights into tumor angiogenesis and vascular polymorphism, referring to the variability in vascular structures and patterns.