Towards Realistic 3D Models of Tumor Vascular Networks

SIMPLE SUMMARY: Three-dimensional models of tumor vascular networks are of significant importance for in vitro and in silico investigations of, for example, the efficiency of anti-cancer drugs in an early stage of clinical transition and can be potentially used for the development of in vitro systems as 3D-printable vascular networks to facilitate personalized medicine and randomized controlled clinical trials. In this work, histologic slices of a human pancreatic tumor are used as examples to establish an algorithm-based method that enables the reconstruction of a 3D vascular network model. The advantages of this method are high resolution and accuracy concerning the characteristics of the vascular network (e.g., density, trajectory of vessels). ABSTRACT: For reliable in silico or in vitro investigations in, for example, biosensing and drug delivery applications, accurate models of tumor vascular networks down to the capillary size are essential. Compared to images acquired with conventional medical imaging techniques, digitalized histological tumor slices have a higher resolution, enabling the delineation of capillaries. Volume rendering procedures can then be used to generate a 3D model. However, the preparation of such slices leads to misalignments in relative slice orientation between consecutive slices. Thus, image registration algorithms are necessary to re-align the slices. Here, we present an algorithm for the registration and reconstruction of a vascular network from histologic slices applied to 169 tumor slices. The registration includes two steps. First, consecutive images are incrementally pre-aligned using feature- and area-based transformations. Second, using the previous transformations, parallel registration for all images is enabled. Combining intensity- and color-based thresholds along with heuristic analysis, vascular structures are segmented. A 3D interpolation technique is used for volume rendering. This results in a 3D vascular network with approximately 400–450 vessels with diameters down to 25–30 µm. A delineation of vessel structures with close distance was limited in areas of high structural density. Improvement can be achieved by using images with higher resolution and or machine learning techniques.