Multiparametric magnetic resonance imaging (mpMRI) is emerging as a promising tool in the clinical pathway of prostate cancer (PCa). The registration between a structural and a functional imaging modality, such as T2-weighted (T2w) and diffusion-weighted imaging (DWI) is fundamental in the development of a mpMRI-based computer aided diagnosis (CAD) system for PCa. Here, we propose an automated method to register the prostate gland in T2w and DWI image sequences by a landmark-based affine registration and a non-parametric diffeomorphic registration. An expert operator manually segmented the prostate gland in both modalities on a dataset of 20 patients. Target registration error and Jaccard index, which measures the overlap between masks, were evaluated pre-and post-registration resulting in an improvement of 44% and 21%, respectively. In the future, the proposed method could be useful in the framework of a CAD system for PCa detection and characterization in mpMRI.

Multimodal T2w and DWI Prostate Gland Automated Registration / De Santi, B.; Salvi, M.; Giannini, V.; Meiburger, K. M.; Michielli, N.; Seoni, S.; Regge, D.; Molinari, F.. - ELETTRONICO. - 2019:(2019), pp. 4427-4430. (Intervento presentato al convegno 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 tenutosi a deu nel 2019) [10.1109/EMBC.2019.8856467].

Multimodal T2w and DWI Prostate Gland Automated Registration

De Santi B.;Salvi M.;Giannini V.;Meiburger K. M.;Michielli N.;Seoni S.;Molinari F.
2019

Abstract

Multiparametric magnetic resonance imaging (mpMRI) is emerging as a promising tool in the clinical pathway of prostate cancer (PCa). The registration between a structural and a functional imaging modality, such as T2-weighted (T2w) and diffusion-weighted imaging (DWI) is fundamental in the development of a mpMRI-based computer aided diagnosis (CAD) system for PCa. Here, we propose an automated method to register the prostate gland in T2w and DWI image sequences by a landmark-based affine registration and a non-parametric diffeomorphic registration. An expert operator manually segmented the prostate gland in both modalities on a dataset of 20 patients. Target registration error and Jaccard index, which measures the overlap between masks, were evaluated pre-and post-registration resulting in an improvement of 44% and 21%, respectively. In the future, the proposed method could be useful in the framework of a CAD system for PCa detection and characterization in mpMRI.
2019
978-1-5386-1311-5
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2872998