从2D投影图像进行3D概率分割和体积测量
3D Probabilistic Segmentation and Volumetry from 2D projection images
摘要：
X射线成像快速、便宜并且对于一线护理评估和术中实时成像（例如，C形臂透视）。然而，它遭受投影信息丢失，缺乏重要的体积信息上的许多必要的诊断生物标志物是based. In本文中，我们探讨概率方法重建三维体积图像从二维成像模式和测量模型的性能和置信度。我们展示了我们的模型在大型连接结构上的性能，并测试了精细结构和图像域灵敏度的限制。我们利用2D-3D卷积网络的快速端到端训练，在117个CT扫描上评估我们的方法，从数字重建X射线照片（DRR）中分割3D结构，Dice评分为$0.91 \pm 0.0013$。源代码将在会议期间提供。
英文摘要：
X-Ray imaging is quick, cheap and useful for front-line care assessment and intra-operative real-time imaging (e.g., C-Arm Fluoroscopy). However, it suffers from projective information loss and lacks vital volumetric information on which many essential diagnostic biomarkers are based on. In this paper we explore probabilistic methods to reconstruct 3D volumetric images from 2D imaging modalities and measure the models' performance and confidence. We show our models' performance on large connected structures and we test for limitations regarding fine structures and image domain sensitivity. We utilize fast end-to-end training of a 2D-3D convolutional networks, evaluate our method on 117 CT scans segmenting 3D structures from digitally reconstructed radiographs (DRRs) with a Dice score of $0.91 \pm 0.0013$. Source code will be made available by the time of the conference.
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。
