Department of Computer Science, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland.
J Digit Imaging. 2012 Dec;25(6):802-14. doi: 10.1007/s10278-012-9460-z.
In this paper, we propose a novel technique for skull stripping of infant (neonatal) brain magnetic resonance images using prior shape information within a graph cut framework. Skull stripping plays an important role in brain image analysis and is a major challenge for neonatal brain images. Popular methods like the brain surface extractor (BSE) and brain extraction tool (BET) do not produce satisfactory results for neonatal images due to poor tissue contrast, weak boundaries between brain and non-brain regions, and low spatial resolution. Inclusion of prior shape information helps in accurate identification of brain and non-brain tissues. Prior shape information is obtained from a set of labeled training images. The probability of a pixel belonging to the brain is obtained from the prior shape mask and included in the penalty term of the cost function. An extra smoothness term is based on gradient information that helps identify the weak boundaries between the brain and non-brain region. Experimental results on real neonatal brain images show that compared to BET, BSE, and other methods, our method achieves superior segmentation performance for neonatal brain images and comparable performance for adult brain images.
本文提出了一种新的技术，用于在图割框架内使用先验形状信息对婴儿（新生儿）脑磁共振图像进行头骨剥离。头骨剥离在脑图像分析中起着重要作用，是新生儿脑图像的主要挑战。由于组织对比度差、脑区与非脑区之间边界较弱以及空间分辨率低，像大脑表面提取器（BSE）和大脑提取工具（BET）这样的流行方法对于新生儿图像不能产生令人满意的结果。包含先验形状信息有助于准确识别脑和非脑组织。先验形状信息是从一组标记的训练图像中获得的。像素属于脑的概率是从先验形状掩模获得的，并包含在代价函数的惩罚项中。一个额外的平滑项基于梯度信息，有助于识别脑区和非脑区之间的弱边界。对真实新生儿脑图像的实验结果表明，与 BET、BSE 和其他方法相比，我们的方法对新生儿脑图像具有更好的分割性能，对成人脑图像具有可比的性能。
