Validation Framework for Analyzing Complex Anatomical Structures: Application of L-System Models

Accurate morphological and topological analysis of renal vascular trees is crucial for understanding vascular architecture, detecting patterns, and identifying pathological deviations. However, the complexity of renal vascular trees, particularly in corrosive endocasts, requires custom-designed algorithms to address unique challenges in reconstruction, segmentation, skeletonization, and graph-based representation. A key issue is the lack of a direct method to validate the correctness of these parameters in the absence of a ground truth prior to pattern analysis, with manual validation being infeasible due to the intricate nature of the renal vasculature. To address this challenge, we introduce a systematic validation framework utilizing artificially generated renal vascular models. We developed computational models using L-systems and fabricated them via 3D printing. These models were scanned using microcomputed tomography ( $\mu $ -CT), simulating the process for in vivo endocasts. We then applied the same computational pipeline to analyze the synthetic models, comparing the derived vascular parameters with the known geometries. Our results demonstrate that the computational methods can accurately replicate the morphology and topology of artificial models, with quantified validation showing high precision in the analysis. This approach provides a reliable methodology for validating vascular analysis algorithms, setting a benchmark for accurate morphological and topological assessments in complex vascular networks. It also highlights the value of synthetic models in verifying algorithm integrity, offering a solid foundation for future research on the analysis of complex anatomical structures.