Example Based Lesion Segmentation.
Example Based Lesion Segmentation.
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基于示例的病变分割。
DOI：
10.1117/12.2043917
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Pham,Dzung
中科院分区：
文献类型：
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作者：
Roy,Snehashis;He,Qing;Carass,Aaron;Jog,Amod;Cuzzocreo,JenniferL;Reich,DanielS;Prince,Jerry;Pham,Dzung
自动、准确地检测白质病变是了解许多疾病（如阿尔茨海默病或多发性硬化症）进展的重要一步。多模态 MR 图像通常用于分割可代表脱髓鞘或缺血区域的 T2 白质病变。一些自动病变分割方法使用生成模型描述病变强度，然后通过启发式和成本最小化的某种组合对病变进行分类。相比之下，我们提出了一种基于补丁的方法，其中使用包含多模态 MR 图像的图集中的示例和相应的手动描绘病变来发现病变。来自受试者 MR 图像的斑块与来自图谱的斑块进行匹配，并根据斑块相似性权重找到病变成员资格。我们对 43 名 MS 受试者进行了实验，他们的扫描显示了不同程度的病变负荷。与最先进的基于模型的病变分割方法相比，我们证明了 Dice 系数和总病变体积的显着改进，表明病变的描绘更加准确。
Automatic and accurate detection of white matter lesions is a significant step toward understanding the progression of many diseases, like Alzheimer’s disease or multiple sclerosis. Multi-modal MR images are often used to segment T2 white matter lesions that can represent regions of demyelination or ischemia. Some automated lesion segmentation methods describe the lesion intensities using generative models, and then classify the lesions with some combination of heuristics and cost minimization. In contrast, we propose a patch-based method, in which lesions are found using examples from an atlas containing multi-modal MR images and corresponding manual delineations of lesions. Patches from subject MR images are matched to patches from the atlas and lesion memberships are found based on patch similarity weights. We experiment on 43 subjects with MS, whose scans show various levels of lesion-load. We demonstrate significant improvement in Dice coefficient and total lesion volume compared to a state of the art model-based lesion segmentation method, indicating more accurate delineation of lesions.