## 1 Introduction  
Non-invasive imaging is playing an increasingly important role in the diagnosis and monitoring of skin tumors, as well as in the study of vascular structures [1]. High-resolution line-field confocal optical coherence tomography (LC-OCT) has proven to be a promising technology for three-dimensional (3D) imaging of tissue. Compared to conventional methods, LC-OCT enables a more detailed in-vivo visualization of skin layers and underlying vessels [2, 3, 4, 5]. The study of tumor vessels is of crucial importance as it provides deeper insights into the mechanisms of tumor development and metastasis. Tumor angiogenesis, the formation of new blood vessels by tumor cells, plays a decisive role in supplying tumors with oxygen and nutrients, enabling their further growth and metastasis [6]. A central mechanism of this angiogenesis is the production of vascular endothelial growth factor (VEGF) by tumor cells under hypoxia, which stimulates the proliferation and migration of endothelial cells [7]. In addition to VEGF, other growth factors such as fibroblast growth factor (FGF) and platelet-derived growth factor (PDGF), as well as hypoxia-inducible factor 1 (HIF-1) are also involved in the regulation of angiogenesis [8, 9]. Therapeutic approaches focus on inhibiting tumor angiogenesis in order to stop tumor growth [10, 11]. Despite these advances, structural abnormalities in tumor vessels and the ability of tumors to activate alternative angiogenic signaling pathways continue to pose challenges. These irregular and often leaky vessels lead to inefficient blood supply and promote tumor cell dissemination [11]. Hematogenous metastasis involves tumor cell invasion into the bloodstream, survival in the circulation, and metastasis to other organs, which is facilitated by interactions with endothelial cells and the involvement of integrins and matrix metalloproteinases (MMPs) [12; 13; 14; 15]. The accurate reconstruction and visualization of vascular structures are critical for advancing our understanding of both normal and pathological processes in vascular biology. Vascular networks are fundamental to numerous physiological functions, and their disruption or abnormal formation is implicated in a wide range of diseases, including cancer and chronic inflammatory conditions. To study these complex networks in detail, particularly in a 3D context, advanced imaging and analysis techniques are essential [3]. This paper focuses on a methodical approach for reconstructing and visualizing vascular structures from 3D image stacks, which involves three key steps as described in the materials and methods section. In addition to manual reconstruction methods, automated methods are increasingly being developed in OCT imaging to improve efficiency and the associated scalability and accuracy. [PERSON] et al. provide a comprehensive overview of automated segmentation and classification methods based on machine learning. These approaches not only enable precise segmentation of complex vascular structures, but also classification of pathological patterns, which underpins their relevance for clinical diagnostics and research [16]. The significance of this reconstruction process extends beyond mere visualization. The resulting 3D models are invaluable for various applications, including the study of vascular development, the analysis of tumor vasculature, and the assessment of vascular changes in response to therapeutic interventions. By providing a more detailed and comprehensive view of vascular networks, this approach enhances our ability to diagnose, treat, and understand diseases where vascular pathology plays a central role. This study delineates the significant advantages of utilizing three-dimensional (3D) reconstructions within the framework of Line-field confocal optical coherence tomography (LC-OCT) imaging. [PERSON] et al. have already demonstrated that this technique is highly effective for examining healthy skin; however, the broader field of vascular structures remained largely unexplored in their work [17].