A boosting approach for prostate cancer detection using multi-parametric MRI

Guillaume Lemaître (1, 2) , Joan Massich (1) , Robert Martí (2) , Jordi Freixenet (2) , Vilanova Joan C (2) , Paul Walker (1) , Désiré Sidibé (1) , Fabrice Mériaudeau (1)

1 Le2i - Laboratoire Electronique, Informatique et Image [UMR6306]
2 VICOROB - Visio per computador i robotica

Prostate cancer has been reported as the second most frequently diagnosed men cancers in the world. In the last decades, new imaging techniques based on MRI have been developed in order to improve the diagnosis task of radiologists. In practise, diagnosis can be affected by multiple factors reducing the chance to detect potential lesions. Computer-aided detection and computer-aided diagnosis have been designed to answer to these needs and provide help to radiologists in their daily duties. In this study, we proposed an automatic method to detect prostate cancer from a per voxel manner using 3T multi-parametric Magnetic Resonance Imaging (MRI) and a gradient boosting classifier. The best performances are obtained using all multi-parametric information as well as zonal information. The sensitivity and specificity obtained are 94.7% and 93.0%, respectively and an Area Under Curve (AUC) of 0.968.

Mots clés: Gradient boosting; multi-parametric MRI; prostate cancer; computer-aided diagnosis

Domaines: Traitement du signal et de l'image [eess.SP]

Guillaume Lemaître, Joan Massich, Robert Martí, Jordi Freixenet, Vilanova Joan C, et al.. A boosting approach for prostate cancer detection using multi-parametric MRI. International Conference on Quality Control and Artificial Vision (QCAV) 2015, Jun 2015, Le Creusot, France. ⟨hal-01235890⟩