Computer-aided detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review - Abstract

Prostate cancer is the second most diagnosed cancer of men all over the world.

In the last few decades, new imaging techniques based on Magnetic Resonance Imaging (MRI) have been developed to improve diagnosis. In practice, diagnosis can be affected by multiple factors such as observer variability and visibility and complexity of the lesions. In this regard, computer-aided detection and computer-aided diagnosis systems have been designed to help radiologists in their clinical practice. Research on computer-aided systems specifically focused for prostate cancer is a young technology and has been part of a dynamic field of research for the last 10years. This survey aims to provide a comprehensive review of the state-of-the-art in this lapse of time, focusing on the different stages composing the work-flow of a computer-aided system. We also provide a comparison between studies and a discussion about the potential avenues for future research. In addition, this paper presents a new public online dataset which is made available to the research community with the aim of providing a common evaluation framework to overcome some of the current limitations identified in this survey.

Written by:
Lemaître G, Martí R, Freixenet J, Vilanova JC, Walker PM, Meriaudeau F.   Are you the author?
LE2I-UMR CNRS 6306, Université de Bourgogne, 12 rue de la Fonderie, 71200 Le Creusot, France; ViCOROB, Universitat de Girona, Campus Montilivi, Edifici P4, 17071 Girona, Spain; ViCOROB, Universitat de Girona, Campus Montilivi, Edifici P4, 17071 Girona, Spain; ViCOROB, Universitat de Girona, Campus Montilivi, Edifici P4, 17071 Girona, Spain; Department of Magnetic Resonance, Clínica Girona, Lorenzana 36, 17002 Girona, Spain; LE2I-UMR CNRS 6306, Université de Bourgogne, Avenue Alain Savary, 21000 Dijon, France. ; ; ; ;

Reference: Comput Biol Med. 2015 May 1;60:8-31.
doi: 10.1016/j.compbiomed.2015.02.009


PubMed Abstract
PMID: 25747341

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