## 4 Discussion  
LC-OCT enables high-resolution, depth-resolved imaging that captures the full spatial complexity of vascular networks, often obscured or inadequately represented in 2D images. Moreover, only 3D imaging allows for the accurate representation of the volume of hollow structures such as blood vessels. [PERSON] et al. used confocal reflectance microscopy (RCM) for the three-dimensional reconstruction of vascular structures in eccentric poromas, rare benign skin tumors. This non-invasive technique enabled a detailed visualization of the vascular architecture of the tumor and thus a more precise characterization. The study highlights the advantages of RCM 3D imaging by mapping different vascular patterns in ecrine poromas, which allows real-time in vivo examination without biopsies. This work also supports further research into 3D imaging for the characterization of various skin tumors [22]. Moreover, [PERSON] et al. explored the enhancement of microvascular imaging using Optical Coherence Tomography (OCT) by leveraging 3D neural networks combined with a channel attention mechanism. They developed a specialized 3D neural network designed to process information from multiple OCT data layers, aiming to enhance the accuracy of microvascular imaging. The channel attention mechanism played a crucial role in emphasizing and extracting key features of the microvascular structure. The proposed approach demonstrated significant improvements in image quality compared to conventional OCT techniques, offering clearer and more detailed visualizations of microvessels--vital for both clinical diagnostics and research. These advancements in image processing, driven by machine learning, have the potential to further elevate OCT technology, particularly in vascular imaging applications [23]. Another interesting study from [PERSON] et al. presents a novel approach for diagnosing BCC by combining deep learning (DL) techniques with the analysis of telangiectasia features. Their study demonstrates that integrating DL methods in the evaluation of telangiectasias can significantly enhance diagnostic accuracy. The authors employed a neural network trained on dermoscopic images for BCC detection and incorporated telangiectasia features to optimize model performance. Their findings indicate that this fusion approach not only improves diagnostic reliability but also reduces both false positives and false negatives. This method represents a promising advancement in non-invasive skin cancer diagnosis and has the potential to lead to better clinical outcomes for patients [24]. Like our study, which provides the foundation for the training of future artificial intelligence (AI) models with LC-OCT, the publication by [PERSON] et al. (2019) introduces an enhanced method for vessel segmentation, aimed primarily at optimizing the quantification of capillary networks in skin tissues using optical coherence tomography angiography (OCTA). This approach addresses the limitations of traditional vessel segmentation techniques, which often struggle to accurately capture fine capillaries. The key steps in this methodology include preprocessing to reduce noise and enhance image quality, initial vessel segmentation to differentiate vascular structures from background tissue, and optimization to refine the segmentation and minimize artifacts. The results demonstrate that this optimized method significantly improves the precision of capillary network visualization and quantification compared to existing techniques [25]. [PERSON] et al. previously addressed the automatic determination of BCC lesion areas using OCTA in combination with a skeletonization method. The goal of this study was to precisely determine tumor areas by reducing the complex vascular network to simplified lines. By integrating OCTA imaging with a skeletonization algorithm, the boundary and area of the carcinoma could be automatically identified.