Cvetić Jurica, Šekoranja Bojan, Švaco Marko, Šuligoj Filip
Department of Robotics and Production System Automation, Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, 10002 Zagreb, Croatia.
Bioengineering (Basel). 2025 May 8;12(5):498. doi: 10.3390/bioengineering12050498.
Accurate preoperative image processing in markerless image-guided surgeries is an important task. However, preoperative planning highly depends on the quality of medical imaging data. In this study, a novel algorithm for outer skin layer extraction from head computed tomography (CT) scans is presented and evaluated. Axial, sagittal, and coronal slices are processed separately to generate spatial data. Each slice is binarized using manually defined Hounsfield unit (HU) range thresholding to create binary images from which valid contours are extracted. The individual points of each contour are then projected into three-dimensional (3D) space using slice spacing and origin information, resulting in uniplanar point clouds. These point clouds are then fused through geometric addition into a single enriched triplanar point cloud. A two-step downsampling process is applied, first at the uniplanar level and then after merging, using a voxel size of 1 mm. Across two independent datasets with a total of 83 individuals, the merged cloud approach yielded an average of 11.61% more unique points compared to the axial cloud. The validity of the triplanar point cloud reconstruction was confirmed by a root mean square (RMS) registration error of 0.848 ± 0.035 mm relative to the ground truth models. These results establish the proposed algorithm as robust and accurate across different CT scanners and acquisition parameters, supporting its potential integration into patient registration for markerless image-guided surgeries.
在无标记图像引导手术中进行准确的术前图像处理是一项重要任务。然而，术前规划高度依赖于医学成像数据的质量。在本研究中，提出并评估了一种从头部计算机断层扫描（CT）图像中提取外层皮肤的新算法。分别对轴向、矢状和冠状切片进行处理以生成空间数据。使用手动定义的亨氏单位（HU）范围阈值对每个切片进行二值化处理，以创建二值图像，从中提取有效轮廓。然后，利用切片间距和原点信息将每个轮廓的各个点投影到三维（3D）空间中，得到单平面点云。然后通过几何加法将这些点云融合成一个单一的丰富三平面点云。应用两步下采样过程，首先在单平面级别进行，然后在合并后进行，体素大小为1毫米。在总共83名个体的两个独立数据集中，与轴向点云相比，合并云方法产生的独特点平均多11.61%。相对于真实模型，三平面点云重建的有效性通过均方根（RMS）配准误差0.848±0.035毫米得到证实。这些结果表明，所提出的算法在不同的CT扫描仪和采集参数下具有鲁棒性和准确性，支持其潜在地集成到无标记图像引导手术的患者配准中。
