کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
8689005 1580955 2016 26 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI
کلمات کلیدی
موضوعات مرتبط
علوم زیستی و بیوفناوری علم عصب شناسی روانپزشکی بیولوژیکی
پیش نمایش صفحه اول مقاله
Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI
چکیده انگلیسی
Tumor segmentation is a particularly challenging task in high-grade gliomas (HGGs), as they are among the most heterogeneous tumors in oncology. An accurate delineation of the lesion and its main subcomponents contributes to optimal treatment planning, prognosis and follow-up. Conventional MRI (cMRI) is the imaging modality of choice for manual segmentation, and is also considered in the vast majority of automated segmentation studies. Advanced MRI modalities such as perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI) and magnetic resonance spectroscopic imaging (MRSI) have already shown their added value in tumor tissue characterization, hence there have been recent suggestions of combining different MRI modalities into a multi-parametric MRI (MP-MRI) approach for brain tumor segmentation. In this paper, we compare the performance of several unsupervised classification methods for HGG segmentation based on MP-MRI data including cMRI, DWI, MRSI and PWI. Two independent MP-MRI datasets with a different acquisition protocol were available from different hospitals. We demonstrate that a hierarchical non-negative matrix factorization variant which was previously introduced for MP-MRI tumor segmentation gives the best performance in terms of mean Dice-scores for the pathologic tissue classes on both datasets.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: NeuroImage: Clinical - Volume 12, February 2016, Pages 753-764
نویسندگان
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