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首页> 外文会议>IEEE International Symposium on Biomedical Imaging: From Nano to Macro >Automatic volumetry can reveal visually undetected disease features on brain MR images in temporal lobe epilepsy
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Automatic volumetry can reveal visually undetected disease features on brain MR images in temporal lobe epilepsy

机译：自动容量测定可以在颞叶癫痫的大脑MR图像上显示视觉上未检测到的疾病特征

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Brain structural volumes can be used for automatically classifying subjects into categories like controls and patients. We aimed to automatically separate patients with temporal lobe epilepsy (TLE) with and without hippocampal atrophy on MRI, pTLE and nTLE, from controls, and determine the epileptogenic side. In the proposed framework 83 brain structure volumes are identified using multi-atlas segmentation. We then use structure selection using a divergence measure and classification based on structural volumes, as well as morphological similarities using SVM. A spectral analysis step is used to convert the pairwise measures of similarity between subjects into per-subject features. Up to 96% of pTLE patients were correctly separated from controls using 14 structural brain volumes. The classification method based on spectral analysis was 91% accurate at separating nTLE patients from controls. Right and left hippocampus were sufficient for the lateralization of the seizure focus in the pTLE group and achieved 100% accuracy.
机译：大脑结构量可用于将受试者自动分类为对照和患者等类别。我们的目的是自动将患有MRI和pTLE和nTLE的伴有和不伴有海马萎缩的颞叶癫痫（TLE）患者与对照组分开，并确定致癫痫的一面。在提出的框架中，使用多图集分割识别了83个脑结构体积。然后，我们使用基于发散度量的结构选择和基于结构体积的分类以及使用SVM的形态相似性。频谱分析步骤用于将对象之间的相似度的成对度量转换为每个对象的特征。使用14个结构化的脑部容积，将多达96％的pTLE患者与对照正确分离。基于光谱分析的分类方法在将nTLE患者与对照组分离时的准确度为91％。右海马和左海马足以使pTLE组的癫痫病灶偏侧化，并达到100％的准确性。

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