Cuingnet Rémi, Prevost Raphael, Lesage David, Cohen Laurent D, Mory Benoît, Ardon Roberto
Philips Research Medisys, France.
Med Image Comput Comput Assist Interv. 2012;15(Pt 3):66-74. doi: 10.1007/978-3-642-33454-2_9.
Kidney segmentation in 3D CT images allows extracting useful information for nephrologists. For practical use in clinical routine, such an algorithm should be fast, automatic and robust to contrast-agent enhancement and fields of view. By combining and refining state-of-the-art techniques (random forests and template deformation), we demonstrate the possibility of building an algorithm that meets these requirements. Kidneys are localized with random forests following a coarse-to-fine strategy. Their initial positions detected with global contextual information are refined with a cascade of local regression forests. A classification forest is then used to obtain a probabilistic segmentation of both kidneys. The final segmentation is performed with an implicit template deformation algorithm driven by these kidney probability maps. Our method has been validated on a highly heterogeneous database of 233 CT scans from 89 patients. 80% of the kidneys were accurately detected and segmented (Dice coefficient > 0.90) in a few seconds per volume.
三维CT图像中的肾脏分割可为肾病学家提取有用信息。对于临床常规的实际应用，这样的算法应快速、自动且对造影剂增强和视野具有鲁棒性。通过结合并改进现有技术（随机森林和模板变形），我们证明了构建满足这些要求的算法的可能性。肾脏通过随机森林按照由粗到精的策略进行定位。利用全局上下文信息检测到的它们的初始位置通过一系列局部回归森林进行细化。然后使用分类森林来获得两个肾脏的概率分割。最终分割由这些肾脏概率图驱动的隐式模板变形算法执行。我们的方法已在来自89名患者的233次CT扫描的高度异质数据库上得到验证。每体积在几秒钟内80%的肾脏被准确检测和分割（骰子系数>0.90）。
