Computer-Assisted Applications in Medicine (CAiM), ETH Zurich, Zurich, Switzerland.
Computer Assisted Research and Development (CARD) Group, University Hospital Balgrist, University of Zurich, Zurich, Switzerland.
Int J Comput Assist Radiol Surg. 2017 Jun;12(6):1031-1039. doi: 10.1007/s11548-017-1570-0. Epub 2017 Mar 24.
Planning orthopedic surgeries is commonly performed in computed tomography (CT) images due to the higher contrast of bony structure. However, soft tissues such as muscles and ligaments that may determine the functional outcome of a procedure are not easy to identify in CT, for which fast and accurate segmentation in MRI would be desirable. To be usable in daily practice, such method should provide convenient means of interaction for modifications and corrections, e.g., during perusal by the surgeon or the planning physician for quality control.
We propose an interactive segmentation framework for MR images and evaluate the outcome for segmentation of bones. We use a random forest classification and a random walker-based spatial regularization. The latter enables the incorporation of user input as well as enforcing a single connected anatomical structures, thanks to which a selective sampling strategy is proposed to substantially improve the supervised learning performance.
We evaluated our segmentation framework on 10 patient humerus MRI as well as 4 high-resolution MRI from volunteers. Interactive humerus segmentations for patients took on average 150 s with over 3.5 times time-gain compared to manual segmentations, with accuracies comparable (converging) to that of much longer interactions. For high-resolution data, a novel multi-resolution random walker strategy further reduced the run time over 20 times of the manual segmentation, allowing for a feasible interactive segmentation framework.
We present a segmentation framework that allows iterative corrections leading to substantial speed gains in bone annotation in MRI. This will allow us to pursue semi-automatic segmentations of other musculoskeletal anatomy first in a user-in-the-loop manner, where later less user interactions or perhaps only few for quality control will be necessary as our annotation suggestions improve.
由于骨结构对比度较高，骨科手术的规划通常在计算机断层扫描 (CT) 图像中进行。然而，在 CT 中不易识别可能决定手术功能结果的软组织，如肌肉和韧带，而 MRI 中的快速准确分割则是理想的。为了在日常实践中使用，这种方法应该为修改和更正提供方便的交互手段，例如，在外科医生或规划医生审阅时进行质量控制。
我们提出了一种用于磁共振图像的交互式分割框架，并评估了骨骼分割的结果。我们使用随机森林分类和基于随机游走的空间正则化。后者可以将用户输入纳入其中，并强制形成单一的连通解剖结构，从而提出了一种选择性采样策略，以大大提高监督学习的性能。
我们在 10 例患者肱骨 MRI 和 4 例志愿者高分辨率 MRI 上评估了我们的分割框架。患者的交互式肱骨分割平均需要 150 秒，与手动分割相比时间增益超过 3.5 倍，其准确性与更长时间交互的准确性相当（收敛）。对于高分辨率数据，一种新的多分辨率随机游走策略进一步将运行时间缩短了手动分割的 20 倍以上，从而实现了可行的交互式分割框架。
我们提出了一种分割框架，允许进行迭代修正，从而在 MRI 中的骨骼注释中实现显著的速度提升。这将使我们能够首先以用户参与的方式对其他肌肉骨骼解剖结构进行半自动分割，随着我们的注释建议的改进，之后可能只需要较少的用户交互或仅需要几次交互即可进行质量控制。
