TY  - CONF
EP  - 3141
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032226643&doi=10.1109%2fEMBC.2017.8037522&partnerID=40&md5=fa9baf1447ac7eb55a334b8e06f877dc
N2  - Prostate cancer (CaP) is the second most diagnosed cancer in men all over the world. In the last decades, new imaging techniques based on magnetic resonance imaging (MRI) have been developed improving diagnosis. In practice, diagnosis is affected by multiple factors such as observer variability and visibility and complexity of the lesions. In this regard, computer-aided detection and diagnosis (CAD) systems are being designed to help radiologists in their clinical practice. We propose a CAD system taking advantage of all MRI modalities (i.e., T2-W-MRI, DCE-MRI, diffusion weighted (DW)-MRI, MRSI). The aim of this CAD system was to provide a probabilistic map of cancer location in the prostate. We extensively tested our proposed CAD using different fusion approaches to combine the features provided by each modality. The source code and the dataset have been released. © 2017 IEEE.
ID  - scholars8388
N1  - cited By 26; Conference of 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 ; Conference Date: 11 July 2017 Through 15 July 2017; Conference Code:130871
AV  - none
Y1  - 2017///
TI  - Computer-aided detection for prostate cancer detection based on multi-parametric magnetic resonance imaging
KW  - contrast medium
KW  -  human; male; nuclear magnetic resonance imaging; prostate tumor
KW  -  Contrast Media; Humans; Magnetic Resonance Imaging; Male; Prostatic Neoplasms
SN  - 1557170X
PB  - Institute of Electrical and Electronics Engineers Inc.
A1  - Lemaitre, G.
A1  - Marti, R.
A1  - Rastgoo, M.
A1  - Meriaudeau, F.
SP  - 3138
ER  -