Purpose: Automated distinct bone segmentation from CT scans is widely used in planning and navigation workflows. U-Net variants are known to provide excellent results in supervised semantic segmentation. However, in distinct bone segmentation from upper body CTs a large field of view and a computationally taxing 3D architecture are required. This leads to low-resolution results lacking detail or localisation errors due to missing spatial context when using high-resolution inputs. Methods: We propose to solve this problem by using end-to-end trainable segmentation networks that combine several 3D U-Nets working at different resolutions. Our approach, which extends and generalizes HookNet and MRN, captures spatial information at a lower resolution and skips the encoded information to the target network, which operates on smaller high-resolution inputs. We evaluated our proposed architecture against single resolution networks and performed an ablation study on information concatenation and the number of context networks. Results: Our proposed best network achieves a median DSC of 0.86 taken over all 125 segmented bone classes and reduces the confusion among similar-looking bones in different locations. These results outperform our previously published 3D U-Net baseline results on the task and distinct-bone segmentation results reported by other groups. Conclusion: The presented multi-resolution 3D U-Nets address current shortcomings in bone segmentation from upper-body CT scans by allowing for capturing a larger field of view while avoiding the cubic growth of the input pixels and intermediate computations that quickly outgrow the computational capacities in 3D. The approach thus improves the accuracy and efficiency of distinct bone segmentation from upper-body CT.
翻译:在规划和导航工作流程中广泛使用CT扫描产生的自定义的骨质分解。 U-Net变量已知在监督的语义分解中提供优异结果。 但是,在上体CT的分解中,需要有一个大视野和计算税3D结构。这导致低分辨率结果缺乏细节或本地化错误,原因是使用高分辨率输入时缺少空间背景。方法:我们建议通过将不同分辨率的数个3D U-Net组合起来的端到端训练分解网络来解决这一问题。我们的方法是扩展和概括HookNet和MRN的中间分解,以较低分辨率捕获空间信息,并将编码信息跳过目标网络的3D结构。我们根据单个分辨率网络评估了我们的拟议架构,并对信息分类和上下文网络的数量进行了一个缩略图研究。结果:我们提议的最佳网络在所有125个分解骨骼类中实现了0.86的中位 DSC,并减少了不同地点的类似直径直径网络的直径直径直径直路径直路径直路径的计算方法之间的混乱。这些结果是我们先前的直径直径直径直径直径直径直径直径直径直径直径直径直径直径径径直的网络结构段段段次的计算结果。