Learning Based Segmentation of CT Brain Images: Application to Postoperative Hydrocephalic Scans.
Learning Based Segmentation of CT Brain Images: Application to Postoperative Hydrocephalic Scans.
复制标题
DOI：
10.1109/tbme.2017.2783305
复制
发表时间：
2018-08
期刊：
影响因子：
--
通讯作者：
Schiff SJ
中科院分区：
文献类型：
--
作者：
Cherukuri V;Ssenyonga P;Warf BC;Kulkarni AV;Monga V;Schiff SJ
脑积水是一种脑内脑脊液（CSF）异常积聚的医学病症。将脑图像分割成脑组织和CSF（手术前后，即术前与术后）在评估手术治疗中起着至关重要的作用。术前图像的分割通常是一个相对简单的问题，并且已经得到了很好的研究。然而，由于扭曲的解剖结构和硬膜下血肿聚集压迫大脑，分割术后（术后）计算机断层扫描（CT）扫描变得更具挑战性。大多数基于强度和特征的分割方法无法将硬膜下与脑和CSF分离，因为硬膜下几何形状在不同患者之间变化很大，并且其强度随时间变化。我们通过一种学习方法来解决这个问题，该方法将分割视为像素级的监督分类，即采用具有标记像素身份的CT扫描的训练集。我们的贡献包括：1.）字典学习框架，其学习可以有效地表示来自同一类的测试样本而不好地表示来自其他类的对应样本的类（段）特定字典，2.）相关计算和存储器占用的量化，以及3.）用于分割术后脑积水CT图像的定制训练和测试程序。在乌干达CURE儿童医院获得的婴儿CT脑图像上进行的实验揭示了我们的方法对最先进的替代品的成功。我们还证明了该算法的计算负担较轻，并表现出对训练样本数量的优雅退化，提高其部署潜力。
Hydrocephalus is a medical condition in which there is an abnormal accumulation of cerebrospinal fluid (CSF) in the brain. Segmentation of brain imagery into brain tissue and CSF (before and after surgery, i.e. pre-op vs. post-op) plays a crucial role in evaluating surgical treatment. Segmentation of pre-op images is often a relatively straightforward problem and has been well researched. However, segmenting post-operative (post-op) computational tomographic (CT)-scans becomes more challenging due to distorted anatomy and subdural hematoma collections pressing on the brain. Most intensity and feature based segmentation methods fail to separate subdurals from brain and CSF as subdural geometry varies greatly across different patients and their intensity varies with time. We combat this problem by a learning approach that treats segmentation as supervised classification at the pixel level, i.e. a training set of CT scans with labeled pixel identities is employed. Our contributions include: 1.) a dictionary learning framework that learns class (segment) specific dictionaries that can efficiently represent test samples from the same class while poorly represent corresponding samples from other classes, 2.) quantification of associated computation and memory footprint, and 3.) a customized training and test procedure for segmenting post-op hydrocephalic CT images. Experiments performed on infant CT brain images acquired from the CURE Children’s Hospital of Uganda reveal the success of our method against the state-of-the-art alternatives. We also demonstrate that the proposed algorithm is computationally less burdensome and exhibits a graceful degradation against number of training samples, enhancing its deployment potential.
影响因子：
7.6
作者：
Luo F;Evans JW;Linney NC;Schmidt MH;Gregson PH
通讯作者：
Gregson PH