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Plexiform Neurofibroma tissue classification

机译:多形神经纤维瘤组织分类

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Plexiform Neurofibroma (PN) is a major complication of NeuroFibromatosis-1 (NF1), a common genetic disease that involving the nervous system. PNs are peripheral nerve sheath tumors extending along the length of the nerve in various parts of the body. Treatment decision is based on tumor volume assessment using MRI, which is currently time consuming and error prone, with limited semi-automatic segmentation support. We present in this paper a new method for the segmentation and tumor mass quantification of PN from STIR MRI scans. The method starts with a user-based delineation of the tumor area in a single slice and automatically detects the PN lesions in the entire image based on the tumor connectivity. Experimental results on seven datasets yield a mean volume overlap difference of 25% as compared to manual segmentation by expert radiologist with a mean computation and interaction time of 12 minutes vs. over an hour for manual annotation. Since the user interaction in the segmentation process is minimal, our method has the potential to successfully become part of the clinical workflow.
机译:Plexiform神经纤维瘤(PN)是NeuroFibromatosis-1(NF1)的主要并发症,NF1是一种常见的遗传性疾病,涉及神经系统。 PN是沿神经的长度在身体各个部位延伸的周围神经鞘瘤。治疗决策是基于使用MRI进行的肿瘤体积评估,目前这种方法耗时且容易出错,并且半自动分割支持有限。我们在本文中提出了一种从STIR MRI扫描中进行PN分割和肿瘤质量定量的新方法。该方法首先在单个切片中基于用户描绘肿瘤区域,然后根据肿瘤的连通性自动检测整个图像中的PN病变。与专家放射科医生的手动分割相比,在七个数据集上的实验结果得出的平均体积重叠差为25%,其平均计算和交互时间为12分钟,而人工标注的时间为一个小时。由于细分过程中的用户交互作用极小,因此我们的方法具有成功成为临床工作流程一部分的潜力。

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