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Semi-automatic segmentation software for quantitative clinical brain glioblastoma evaluation.

Abstract：

RATIONALE AND OBJECTIVES:Quantitative measurement provides essential information about disease progression and treatment response in patients with glioblastoma multiforme (GBM). The goal of this article 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. MATERIALS AND METHODS: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 magnetic resonance (MR) brain data, and the latter refines the segmentation results with minimal manual input. RESULTS: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. CONCLUSION: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 MR imaging data. The proposed pipeline could be used as additional information to interpret MR brain images in neuroradiology.

journal_name

Acad Radiol

journal_title

Academic radiology

authors

Zhu Y,Young GS,Xue Z,Huang RY,You H,Setayesh K,Hatabu H,Cao F,Wong ST

doi

10.1016/j.acra.2012.03.026

subject

Has Abstract

pub_date

2012-08-01 00:00:00

pages

977-85

issue

8

eissn

1076-6332

issn

1878-4046

pii

S1076-6332(12)00180-8

journal_volume

19

pub_type

杂志文章

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