论文标题
与对比增强了全身计算机断层扫描的骨分割
Bone Segmentation in Contrast Enhanced Whole-Body Computed Tomography
论文作者
论文摘要
骨骼区域的分割可以增强CT成像中的诊断，疾病表征和治疗监测。相比之下，增强的全身扫描准确的自动分割特别困难，因为低剂量的全身协议降低了图像质量，并且在依靠像素强度的差异时使对比度增强区域更难分离。本文基于训练数据的窗口和Sigmoid激活阈值选择的修改，从低剂量对比增强的全身CT扫描中，基于训练数据的窗口和Sigmoid激活阈值选择的修改，概述了具有新颖的预处理技术的U-NET结构。所提出的方法在两个内部数据集和一个外部测试数据集上达到了平均骰子系数为0.979、0.965和0.934。我们已经证明，适当的预处理对于区分骨骼和对比染料很重要，并且有限的数据可以实现出色的结果。
Segmentation of bone regions allows for enhanced diagnostics, disease characterisation and treatment monitoring in CT imaging. In contrast enhanced whole-body scans accurate automatic segmentation is particularly difficult as low dose whole body protocols reduce image quality and make contrast enhanced regions more difficult to separate when relying on differences in pixel intensities. This paper outlines a U-net architecture with novel preprocessing techniques, based on the windowing of training data and the modification of sigmoid activation threshold selection to successfully segment bone-bone marrow regions from low dose contrast enhanced whole-body CT scans. The proposed method achieved mean Dice coefficients of 0.979, 0.965, and 0.934 on two internal datasets and one external test dataset respectively. We have demonstrated that appropriate preprocessing is important for differentiating between bone and contrast dye, and that excellent results can be achieved with limited data.
