Izard Camille, Jedynak Bruno, Stark Craig E L
Laboratoire Paul Painlevé, Université des Sciences et Technologies de Lille, France.
Med Image Comput Comput Assist Interv. 2006;9(Pt 1):849-56. doi: 10.1007/11866565_104.
In medical imaging, finding landmarks that provide biologically meaningful correspondences is often a challenging and time-consuming manual task. In this paper we propose a generic and simple algorithm for landmarking non-cortical brain structures automatically. We use a probabilistic model of the image intensities based on the deformation of a tissue probability map, learned from a training set of hand-landmarked images. In this setting, estimating the location of the landmarks in a new image is equivalent to finding, by likelihood maximization, the "best" deformation from the tissue probability map to the image. The resulting algorithm is able to handle arbitrary types and numbers of landmarks. We demonstrate our algorithm on the detection of 3 landmarks of the hippocampus in brain MR images.
在医学成像中，找到能提供具有生物学意义对应关系的地标通常是一项具有挑战性且耗时的手动任务。在本文中，我们提出了一种通用且简单的算法，用于自动标记非皮质脑结构的地标。我们基于从一组手动标记图像的训练集中学习到的组织概率图的变形，使用图像强度的概率模型。在这种情况下，估计新图像中地标的位置等同于通过似然最大化找到从组织概率图到图像的“最佳”变形。由此产生的算法能够处理任意类型和数量的地标。我们在脑磁共振图像中检测海马体的3个地标上展示了我们的算法。
