Pauly Olivier, Glocker Ben, Criminisi Antonio, Mateus Diana, Möller Axel Martinez, Nekolla Stephan, Navab Nassir
Computer Aided Medical Procedures, Technische Universität München, Germany.
Med Image Comput Comput Assist Interv. 2011;14(Pt 3):239-47. doi: 10.1007/978-3-642-23626-6_30.
Automatic localization of multiple anatomical structures in medical images provides important semantic information with potential benefits to diverse clinical applications. Aiming at organ-specific attenuation correction in PET/MR imaging, we propose an efficient approach for estimating location and size of multiple anatomical structures in MR scans. Our contribution is three-fold: (1) we apply supervised regression techniques to the problem of anatomy detection and localization in whole-body MR, (2) we adapt random ferns to produce multidimensional regression output and compare them with random regression forests, and (3) introduce the use of 3D LBP descriptors in multi-channel MR Dixon sequences. The localization accuracy achieved with both fern- and forest-based approaches is evaluated by direct comparison with state of the art atlas-based registration, on ground-truth data from 33 patients. Our results demonstrate improved anatomy localization accuracy with higher efficiency and robustness.
医学图像中多个解剖结构的自动定位提供了重要的语义信息，对多种临床应用具有潜在益处。针对PET/MR成像中的器官特异性衰减校正，我们提出了一种在MR扫描中估计多个解剖结构位置和大小的有效方法。我们的贡献有三个方面：（1）我们将监督回归技术应用于全身MR中的解剖结构检测和定位问题；（2）我们采用随机蕨类植物生成多维回归输出，并将其与随机回归森林进行比较；（3）介绍在多通道MR Dixon序列中使用3D LBP描述符。通过与基于最先进图谱配准的方法直接比较，在33例患者的真实数据上评估了基于蕨类植物和森林的方法所实现的定位精度。我们的结果表明，解剖结构定位精度得到了提高，且具有更高的效率和鲁棒性。
