Computer aided-diagnosis of prostate cancer on multiparametric MRI: a technical review of current research

Shijun Wang, Karen Burtt, Baris Turkbey, Peter Choyke, Ronald M Summers, Shijun Wang, Karen Burtt, Baris Turkbey, Peter Choyke, Ronald M Summers

Abstract

Prostate cancer (PCa) is the most commonly diagnosed cancer among men in the United States. In this paper, we survey computer aided-diagnosis (CADx) systems that use multiparametric magnetic resonance imaging (MP-MRI) for detection and diagnosis of prostate cancer. We review and list mainstream techniques that are commonly utilized in image segmentation, registration, feature extraction, and classification. The performances of 15 state-of-the-art prostate CADx systems are compared through the area under their receiver operating characteristic curves (AUC). Challenges and potential directions to further the research of prostate CADx are discussed in this paper. Further improvements should be investigated to make prostate CADx systems useful in clinical practice.

Figures

Figure 1
Figure 1
Workflow of a typical prostate CADx system. Green rectangles indicate data (original scans and images after preprocessing); yellow rectangles indicate processes applied to the data or images.
Figure 2
Figure 2
Illustration of a prostate CAD prediction map showing true positive cancer classified correctly by SVM. The red rectangle indicates an image patch within the cancer in which local image features are extracted from T2WI, ADC, and Ktrans map. The green contour denotes the boundary of the prostate. Bright regions in the CAD prediction map correspond to a high probability of cancer and coincide with the correct location of the cancer.

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