Semi-automatic segmentation software for quantitative clinical brain glioblastoma evaluation.
Semi-automatic segmentation software for quantitative clinical brain glioblastoma evaluation.
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DOI：
10.1016/j.acra.2012.03.026
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发表时间：
2012-08
影响因子：
4.8
通讯作者：
Wong, Stephen T.
中科院分区：
文献类型：
--
作者：
Zhu, Ying;Young, Geoffrey S.;Xue, Zhong;Huang, Raymond Y.;You, Hui;Setayesh, Klan;Hatabu, Hiroto;Cao, Fei;Wong, Stephen T.
定量测量提供有关多形性胶质母细胞瘤 (GBM) 患者疾病进展和治疗反应的重要信息。本文的目标是使用 GBM 患者的临床数据，提出并验证半自动 GBM 分割的软件流程，称为 AFINITI（治疗干预神经影像辅助随访）。我们的软件采用当前最先进的肿瘤分割算法，并将其组合成一个临床可用的流程。传统基于体素的分割和基于可变形形状的分割的优点都嵌入到软件管道中。前者提供基于 T1 和 T2 加权 MR 脑数据的自动肿瘤分割方案，后者以最少的手动输入细化分割结果。对 GBM 患者的 26 幅临床 MR 脑图像进行了处理，并与手动结果进行了比较。结果可以使用嵌入式图形用户界面 (GUI) 进行可视化。使用临床 GBM 数据的验证结果显示 AFINITI 结果与手动注释之间具有高度相关性。与体素分割相比，AFINITI 在从多模态 MRI 数据中分割增强的 GBM 时产生了更准确的结果。所提出的管道可用作解释神经放射学中 MR 脑图像的附加信息。
Quantitative measurement provides essential information about disease progression and treatment response in patients with Glioblastoma multiforme (GBM). The goal of this paper is to present and validate a software pipeline for semi-automatic GBM segmentation, called AFINITI (Assisted Follow-up in NeuroImaging of Therapeutic Intervention), using clinical data from GBM patients. Our software adopts the current state-of-the-art tumor segmentation algorithms and combines them into one clinically usable pipeline. Both the advantages of the traditional voxel-based and the deformable shape-based segmentation are embedded into the software pipeline. The former provides an automatic tumor segmentation scheme based on T1- and T2-weighted MR brain data, and the latter refines the segmentation results with minimal manual input. Twenty six clinical MR brain images of GBM patients were processed and compared with manual results. The results can be visualized using the embedded graphic user interface (GUI). Validation results using clinical GBM data showed high correlation between the AFINITI results and manual annotation. Compared to the voxel-wise segmentation, AFINITI yielded more accurate results in segmenting the enhanced GBM from multimodality MRI data. The proposed pipeline could be used as additional information to interpret MR brain images in neuroradiology.
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影响因子：
158.5
作者：
Stupp, R;Mason, WP;Ryan, G
通讯作者：
Ryan, G
DOI：
10.3322/caac.20069
发表时间：
2010-05
期刊：
CA: a cancer journal for clinicians
影响因子：
--
作者：
Van Meir EG;Hadjipanayis CG;Norden AD;Shu HK;Wen PY;Olson JJ
通讯作者：
Olson JJ
影响因子：
2.5
作者：
VAIDYANATHAN, M;CLARKE, LP;SILBIGER, M
通讯作者：
SILBIGER, M
影响因子：
45.3
作者：
MACDONALD, DR;CASCINO, TL;CAIRNCROSS, JG
通讯作者：
CAIRNCROSS, JG
影响因子：
9.9
作者：
Norden, A. D.;Young, G. S.;Wen, P. Y.
通讯作者：
Wen, P. Y.