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.
为确保在生物传感和药物递送等领域的可靠计算机模拟或体外研究，建立精确至毛细血管尺度的肿瘤血管网络模型至关重要。相较于传统医学成像技术获取的图像，数字化组织学肿瘤切片具有更高分辨率，能够清晰勾勒毛细血管结构。通过体绘制技术可进一步生成三维模型。然而，切片制备过程会导致连续切片间的相对取向偏差，因此需要采用图像配准算法进行重对齐。本研究提出一种针对169张肿瘤组织切片的血管网络配准与重建算法。该算法包含两个步骤：首先采用基于特征和区域的变换对连续图像进行增量预配准；随后利用前期变换实现所有图像的并行配准。通过结合基于强度和颜色的阈值分割与启发式分析，实现血管结构分割，并运用三维插值技术进行体绘制。最终构建出包含约400-450条血管的三维血管网络，最小血管直径达25-30微米。在高密度结构区域，紧密相邻的血管结构描绘存在局限，可通过采用更高分辨率图像或机器学习技术予以改进。
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