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 92.77% and impressive robustness against class imbalance. Our qualitative evaluation of standardized reconstruction results demonstrates the clinical practicability and validity of our method.

翻译：三维（3D）头部计算机断层扫描（CT）图像重建能够揭示组织结构的复杂空间关系，从而辅助精准诊断。然而，在临床环境中，由于技术人员定位不佳、患者身体限制或CT扫描仪倾斜角度限制，获取无偏差的理想头部CT扫描颇具挑战。人工格式化与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，我们提出了一种高效的自动头部CT图像三维重建方法，以提高准确性和可重复性，并减少人工干预。本方法采用基于深度学习的目标检测算法，识别并评估眶耳线标志，在重建前自动重定图像格式。鉴于当前缺乏针对头部CT图像领域目标检测算法的系统评估，我们从理论和实验角度比较了十种方法。通过探索其精度、效率及鲁棒性，我们选定轻量级YOLOv8作为最适合本任务的算法，其平均精度均值（mAP）达92.77%，并展现出对类别不平衡的出色鲁棒性。对标准化重建结果的定性评估验证了本方法的临床实用性与有效性。