Middleton Ian, Damper Robert I
Microsoft Corporation, One Microsoft Way, Redmond, WA 98052, USA.
Med Eng Phys. 2004 Jan;26(1):71-86. doi: 10.1016/s1350-4533(03)00137-1.
Segmentation of medical images is very important for clinical research and diagnosis, leading to a requirement for robust automatic methods. This paper reports on the combined use of a neural network (a multilayer perceptron, MLP) and active contour model ('snake') to segment structures in magnetic resonance (MR) images. The perceptron is trained to produce a binary classification of each pixel as either a boundary or a non-boundary point. Subsequently, the resulting binary (edge-point) image forms the external energy function for a snake, used to link the candidate boundary points into a continuous, closed contour. We report here on the segmentation of the lungs from multiple MR slices of the torso; lung-specific constraints have been avoided to keep the technique as general as possible. In initial investigations, the inputs to the MLP were limited to normalised intensity values of the pixels from an (7 x 7) window scanned across the image. The use of spatial coordinates as additional inputs to the MLP is then shown to provide an improvement in segmentation performance as quantified using the effectiveness measure (a weighted product of precision and recall). Training sets were first developed using a lengthy iterative process. Thereafter, a novel cost function based on effectiveness is proposed for training that allows us to achieve dramatic improvements in segmentation performance, as well as faster, non-iterative selection of training examples. The classifications produced using this cost function were sufficiently good that the binary image produced by the MLP could be post-processed using an active contour model to provide an accurate segmentation of the lungs from the multiple slices in almost all cases, including unseen slices and subjects.
医学图像分割对于临床研究和诊断非常重要，因此需要强大的自动方法。本文报道了神经网络（多层感知器，MLP）与活动轮廓模型（“蛇形模型”）相结合用于分割磁共振（MR）图像中的结构。训练感知器以对每个像素进行二分类，即判断其是边界点还是非边界点。随后，得到的二值（边缘点）图像形成蛇形模型的外部能量函数，用于将候选边界点连接成连续的闭合轮廓。我们在此报告从躯干的多个MR切片中分割肺部的情况；为了使该技术尽可能通用，避免了使用肺部特定的约束条件。在初步研究中，MLP的输入仅限于在图像上扫描的（7×7）窗口内像素的归一化强度值。然后表明，将空间坐标作为MLP的额外输入可提高分割性能，这通过有效性度量（精度和召回率的加权乘积）来量化。首先使用冗长的迭代过程开发训练集。此后，提出了一种基于有效性的新颖代价函数用于训练，这使我们能够在分割性能上实现显著提升，以及更快地非迭代选择训练示例。使用此代价函数产生的分类足够好，以至于MLP产生的二值图像几乎在所有情况下（包括未见切片和受试者）都可以使用活动轮廓模型进行后处理，以从多个切片中准确分割出肺部。
