论文标题
来自2D投影图像的3D概率分割和体积
3D Probabilistic Segmentation and Volumetry from 2D projection images
论文作者
论文摘要
X射线成像快速，便宜且可用于前线护理评估和术中实时成像（例如C-ARM荧光镜检查）。但是，它遭受了投射信息丢失的损失，并且缺乏许多基本诊断生物标志物所基于的重要体积信息。在本文中，我们探讨了从2D成像方式重建3D体积图像的概率方法，并测量模型的性能和置信度。我们显示了模型在大型连接结构上的性能，并测试了有关细胞结构和图像域敏感性的局限性。我们利用2D-3D卷积网络的快速端到端培训，对我们的方法评估我们的方法，从数字重建的X光片（DRRS）中分割了117 CT扫描，其骰子得分为$ 0.91 \ pm 0.0013 $。源代码将在会议时提供。
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.
