Please use this identifier to cite or link to this item:
Title: Cross-Device Automated Prostate Cancer Localization With Multiparametric MRI
Authors: Artan, Yusuf
Oto, Aytekin
Yetik, İmam Şamil
Keywords: Intensity normalization
magnetic resonance imaging (MRI)
prostate cancer
discriminant analysis
Issue Date: 2013
Publisher: IEEE-Inst Electrical Electronics Engineers Inc
Abstract: Prostate cancer localization using supervised classification techniques has aroused considerable interest in medical imaging community in recent years. However, it is crucial to have an accurate training data set for supervised classification techniques. Since different devices with, e.g., different protocols and/or field strengths cause different intensity profiles, each device/protocol must have an accompanying training data set, which is very costly to obtain. It is highly desirable to adapt the existing classifier(s) trained for one device/protocol to help classify data coming from another device/protocol. In this paper, we propose a novel method that has the ability to design classifiers obtained from one imaging protocol and/or MRI device to be used on a data set from another protocol and/or imaging device. As an example problem, we consider prostate cancer localization with multiparametric MRI. We show that simple normalization techniques such as z-score are not sufficient for cross-device automated cancer localization. On the other hand, the method we have originally developed based on relative intensity allows us to successfully use a classifier obtained from one device to be applied on a test patient imaged with another device. Proposed method also allows us to employ T2-weighted MR images directly instead of an additional step to normalize T2-weighted images usually performed in an ad hoc manner when T2 maps are not available. To demonstrate the effectiveness of the proposed method, we use a multiparametric MRI data set acquired from 18 biopsy-confirmed cancer patients with two separate scanners: 1) 1.5-T (Excite HD) GE and 2) 1.5-T (Achieva) Philips Healthcare scanners. A comprehensive visual, quantitative, and statistical analysis of the results show that methods we have developed allow us to: 1) perform cross-device automated classification and 2) use T2-weighted images without an ad hoc subject-specific normalization.
ISSN: 1057-7149
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Show full item record

CORE Recommender


checked on Sep 23, 2022


checked on Sep 24, 2022

Page view(s)

checked on Dec 26, 2022

Google ScholarTM



Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.