This work focuses the attention on the segmentation of meningioma and peritumoral edema from multispectral brain MR imagery. Precise tumour and edema delineation and volume quantification from preoperative MRI data contribute to formulate surgical indications in elderly patients harbouring intracranial meningioma. The authors propose a fully automatic procedure based on the allied use of Graph Cut and support vector machine. The overall strategy combines the advantages of the image-based and machine learning techniques adopted, optimising the balancing between accuracy and stability/reproducibility of the results. Experimental results, obtained by processing in-house collected data, prove that the method is robust and oriented to the use in clinical practice.

Meningioma and peritumoral edema segmentation of preoperative MRI brain scans

BINAGHI, ELISABETTA;BALBI, SERGIO
2018-01-01

Abstract

This work focuses the attention on the segmentation of meningioma and peritumoral edema from multispectral brain MR imagery. Precise tumour and edema delineation and volume quantification from preoperative MRI data contribute to formulate surgical indications in elderly patients harbouring intracranial meningioma. The authors propose a fully automatic procedure based on the allied use of Graph Cut and support vector machine. The overall strategy combines the advantages of the image-based and machine learning techniques adopted, optimising the balancing between accuracy and stability/reproducibility of the results. Experimental results, obtained by processing in-house collected data, prove that the method is robust and oriented to the use in clinical practice.
2018
http://www.tandfonline.com/loi/tciv20
brain tumour segmentation; edema segmentation; Graph Cut; Magnetic resonance imaging; meningioma segmentation; support vector machine;
Binaghi, Elisabetta; Pedoia, Valentina; Balbi, Sergio
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11383/2052135
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