Andrews Shawn, Hamarneh Ghassan, Saad Ahmed
Medical Image Analysis Lab, Simon Fraser University, Canada.
Med Image Comput Comput Assist Interv. 2010;13(Pt 3):9-16. doi: 10.1007/978-3-642-15711-0_2.
Updating segmentation results in real-time based on repeated user input is a reliable way to guarantee accuracy, paramount in medical imaging applications, while making efficient use of an expert's time. The random walker algorithm with priors is a robust method able to find a globally optimal probabilistic segmentation with an intuitive method for user input. However, like many other segmentation algorithms, it can be too slow for real-time user interaction. We propose a speedup to this popular algorithm based on offline precomputation, taking advantage of the time images are stored on servers prior to an analysis session. Our results demonstrate the benefits of our approach. For example, the segmentations found by the original random walker and by our new precomputation method for a given 3D image have a Dice's similarity coefficient of 0.975, yet our method runs in 1/25th of the time.
基于用户的重复输入实时更新分割结果是保证准确性的可靠方法，这在医学成像应用中至关重要，同时能有效利用专家的时间。带先验的随机游走算法是一种强大的方法，能够通过直观的用户输入方式找到全局最优概率分割。然而，与许多其他分割算法一样，它对于实时用户交互来说可能太慢。我们基于离线预计算提出了对这种流行算法的加速方法，利用分析会话之前图像存储在服务器上的时间。我们的结果证明了我们方法的优势。例如，对于给定的3D图像，原始随机游走算法和我们新的预计算方法找到的分割结果的骰子相似系数为0.975，但我们的方法运行时间仅为其1/25。
