IEEE Trans Image Process - Cross-Device Automated Prostate Cancer Localization With Multiparametric MRI.

Tópicos

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{ imag(2675) segment(2577) method(1081) }
{ cancer(2502) breast(956) screen(824) }
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{ framework(1458) process(801) describ(734) }
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Resumo

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 dataset 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 dataset 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 dataset 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 dataset acquired from 18 biopsyconfirmed cancer patients with two separate scanners : (i) 1.5- T (Excite HD) GE and (ii) 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, (i) perform cross-device automated classification, (ii) use T2-weighted images without an ad hoc subject-specific normalization.

Resumo Limpo

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