Abstract

Tumor cells in cerebral glioma invade the surrounding tissues preferentially along white-matter tracts, spreading beyond the abnormal area seen on conventional MR images. Diffusion Tensor Imaging can reveal large peritumoral abnormalities in gliomas, which are not apparent on MRI. Our aim was to characterize pathological vs. healthy tissue in DTI datasets by 3D statistical Texture Analysis, developing an automatic segmentation technique (CAD, Computer Assisted Detection) for cerebral glioma based on a supervised classifier (an artificial neural network). A Matlab GUI (Graphical User Interface) was created to help the physician in the assisted diagnosis process and to optimize interactivity with the segmentation system, especially for patient follow-up during chemotherapy, and for preoperative assessment of tumor extension. Preliminary tissue classification results were obtained for the p map (the calculated area under the ROC curve, AUC, was 0.96) and the FAmap (AUC¼0.98). Test images were automatically segmented by tissue classification; manual and automatic segmentations were compared, showing good concordance.

Autore Pugliese

• De Nunzio Giorgio

Tutti gli autori

• G. De Nunzio , G. Pastore , M. Donativi , A. Castellano , A. Falini

Titolo volume/Rivista

NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH. SECTION A, ACCELERATORS, SPECTROMETERS, DETECTORS AND ASSOCIATED EQUIPMENT

Anno di pubblicazione

2011

ISSN

0168-9002

ISBN

Non Disponibile

Numero di citazioni Wos

6

Ultimo Aggiornamento Citazioni

28/04/2018

Numero di citazioni Scopus

6

Ultimo Aggiornamento Citazioni

28/04/2018

Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile