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神经纤维瘤组织的交互式分割:方法和初步性能评估。

Interactive segmentation of plexiform neurofibroma tissue: method and preliminary performance evaluation.

机构信息

School of Engineering and Computer Science, The Hebrew University of Jerusalem, Jerusalem, Israel.

出版信息

Med Biol Eng Comput. 2012 Aug;50(8):877-84. doi: 10.1007/s11517-012-0929-1. Epub 2012 Jun 16.

Abstract

Plexiform neurofibromas (PNs) are a major manifestation of neurofibromatosis-1 (NF1), a common genetic disease involving the nervous system. Treatment decisions are mostly based on a gross assessment of changes in tumor using MRI. Accurate volumetric measurements are rarely performed in this kind of tumors mainly due to its great dispersion, size, and multiple locations. This paper presents a semi-automatic method for segmentation of PN from STIR MRI scans. The method starts with a user-based delineation of the tumor area in a single slice and automatically segments the PN lesions in the entire image based on the tumor connectivity. Experimental results on seven datasets, with lesion volumes in the range of 75-690 ml, yielded a mean absolute volume error of 10 % (after manual adjustment) as compared to manual segmentation by an expert radiologist. The mean computation and interaction time was 13 versus 63 min for manual annotation.

摘要

丛状神经纤维瘤(PN)是神经纤维瘤病-1(NF1)的主要表现之一,是一种常见的神经系统遗传疾病。治疗决策主要基于 MRI 对肿瘤变化的粗略评估。由于其分布广泛、大小不一和位置多样,这种肿瘤很少进行准确的体积测量。本文提出了一种基于 STIR MRI 扫描的丛状神经纤维瘤半自动分割方法。该方法首先在单张切片上基于用户的肿瘤区域进行勾画,然后根据肿瘤连通性自动分割整个图像中的 PN 病变。在七个数据集上的实验结果表明,与专家放射科医生的手动分割相比,病变体积在 75-690ml 范围内的平均绝对体积误差为 10%(经过手动调整)。手动注释的平均计算和交互时间分别为 13 分钟和 63 分钟。

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