利用空间和形状先验知识对中风患者脑血管病变进行分割。
Segmentation of cerebrovascular pathologies in stroke patients with spatial and shape priors.
作者信息
Dalca Adrian Vasile, Sridharan Ramesh, Cloonan Lisa, Fitzpatrick Kaitlin M, Kanakis Allison, Furie Karen L, Rosand Jonathan, Wu Ona, Sabuncu Mert, Rost Natalia S, Golland Polina
出版信息
Med Image Comput Comput Assist Interv. 2014;17(Pt 2):773-80. doi: 10.1007/978-3-319-10470-6_96.
We propose and demonstrate an inference algorithm for the automatic segmentation of cerebrovascular pathologies in clinical MR images of the brain. Identifying and differentiating pathologies is important for understanding the underlying mechanisms and clinical outcomes of cerebral ischemia. Manual delineation of separate pathologies is infeasible in large studies of stroke that include thousands of patients. Unlike normal brain tissues and structures, the location and shape of the lesions vary across patients, presenting serious challenges for prior-driven segmentation. Our generative model captures spatial patterns and intensity properties associated with different cerebrovascular pathologies in stroke patients. We demonstrate the resulting segmentation algorithm on clinical images of a stroke patient cohort.
我们提出并演示了一种用于在脑部临床磁共振成像中自动分割脑血管病变的推理算法。识别和区分病变对于理解脑缺血的潜在机制和临床结果很重要。在包含数千名患者的大型中风研究中，手动描绘单独的病变是不可行的。与正常脑组织和结构不同，病变的位置和形状在患者之间各不相同，这给基于先验的分割带来了严峻挑战。我们的生成模型捕捉了与中风患者不同脑血管病变相关的空间模式和强度特性。我们在一组中风患者的临床图像上展示了由此产生的分割算法。
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