Convex non-negative matrix factorization for brain tumor delimitation from MRSI data

凸非负矩阵分解用于从 MRSI 数据中划分脑肿瘤

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作者:Sandra Ortega-Martorell, Paulo J G Lisboa, Alfredo Vellido, Rui V Simões, Martí Pumarola, Margarida Julià-Sapé, Carles Arús

Background

Pattern Recognition techniques can provide invaluable insights in the field of neuro-oncology. This is because the clinical analysis of brain tumors requires the use of non-invasive

Significance

The unsupervised nature of Convex-NMF, which does not use prior information regarding the tumor area for its delimitation, places this approach one step ahead of classical label-requiring supervised methods for discrimination between tissue types, minimizing the negative effect of using mislabeled voxels. Convex-NMF also relaxes the non-negativity constraints on the observed data, which allows for a natural representation of the MRSI signal. This should help radiologists to accurately tackle one of the main sources of uncertainty in the clinical management of brain tumors, which is the difficulty of appropriately delimiting the pathological area.

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