Three-dimensional (3D) reconstruction of head Computed Tomography (CT) images elucidates the intricate spatial relationships of tissue structures, thereby assisting in accurate diagnosis. Nonetheless, securing an optimal head CT scan without deviation is challenging in clinical settings, owing to poor positioning by technicians, patient's physical constraints, or CT scanner tilt angle restrictions. Manual formatting and reconstruction not only introduce subjectivity but also strain time and labor resources. To address these issues, we propose an efficient automatic head CT images 3D reconstruction method, improving accuracy and repeatability, as well as diminishing manual intervention. Our approach employs a deep learning-based object detection algorithm, identifying and evaluating orbitomeatal line landmarks to automatically reformat the images prior to reconstruction. Given the dearth of existing evaluations of object detection algorithms in the context of head CT images, we compared ten methods from both theoretical and experimental perspectives. By exploring their precision, efficiency, and robustness, we singled out the lightweight YOLOv8 as the aptest algorithm for our task, with an mAP of 92.91% and impressive robustness against class imbalance. Our qualitative evaluation of standardized reconstruction results demonstrates the clinical practicability and validity of our method.

翻译：头部计算机断层扫描（CT）图像的三维重建能够清晰呈现组织结构的复杂空间关系，从而辅助精准诊断。然而，由于操作人员定位不当、患者身体条件限制或CT扫描仪倾斜角度限制，在临床环境中获得无偏差的优质头部CT扫描具有挑战性。手动格式化和重建不仅引入主观性，还耗费时间和人力资源。为解决这些问题，我们提出一种高效的自动头部CT图像三维重建方法，可提高准确性和可重复性，同时减少人工干预。该方法采用基于深度学习的目标检测算法，通过识别和评估眶耳线标志，在重建前自动对图像进行重格式化。鉴于目前缺乏针对头部CT图像目标检测算法的系统评估，我们从理论和实验角度对比了十种方法。通过探索其精度、效率和鲁棒性，筛选出轻量级YOLOv8作为最适合本任务的算法，其平均精度（mAP）达92.91%，且对类别不平衡具有显著鲁棒性。对标准化重建结果的定性评估证明了该方法的临床实用性和有效性。