用于监测病灶生长的纵向脊柱CT配准
Registration of Longitudinal Spine CTs for Monitoring Lesion Growth
AI总结：
提出一种两步流程的纵向脊柱CT自动配准方法，通过深度学习定位椎骨并生成3D表面，再利用高斯混合模型进行配准，实验证明其能准确评估病灶进展。
摘要：
纵向脊柱图像的准确可靠配准对于评估疾病进展和手术效果至关重要。实现全自动且鲁棒的配准对临床应用很重要，但由于病灶导致的形状和外观的显著变化，这具有挑战性。本文提出一种自动对齐纵向脊柱CT并准确评估病灶进展的新方法。该方法采用两步流程：首先使用深度学习模型自动定位和标记椎骨并生成3D表面，然后使用高斯混合模型表面配准进行纵向对齐。我们在5名患者的37个椎骨上测试了该方法，使用基线CT及3、6和12个月的随访，共进行111次配准。实验显示配准准确，平均Hausdorff距离为0.65 mm，平均Dice分数为0.92。
英文摘要：
Accurate and reliable registration of longitudinal spine images is essential for assessment of disease progression and surgical outcome. Implementing a fully automatic and robust registration is crucial for clinical use, however, it is challenging due to substantial change in shape and appearance due to lesions. In this paper we present a novel method to automatically align longitudinal spine CTs and accurately assess lesion progression. Our method follows a two-step pipeline where vertebrae are first automatically localized, labeled and 3D surfaces are generated using a deep learning model, then longitudinally aligned using a Gaussian mixture model surface registration. We tested our approach on 37 vertebrae, from 5 patients, with baseline CTs and 3, 6, and 12 months follow-ups leading to 111 registrations. Our experiment showed accurate registration with an average Hausdorff distance of 0.65 mm and average Dice score of 0.92.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
