Computer-Aided Diagnosis

Detection of prostate cancer in multiparametric MRI using random forest with instance weighting

[+] Author Affiliations
Nathan Lay, Yohannes Tsehay, Ronald M. Summers

National Institutes of Health, Clinical Center, Imaging Biomarkers and Computer Aided Diagnosis Laboratory, Bethesda, Maryland, United States

Matthew D. Greer, Baris Turkbey, Peter L. Choyke, Peter Pinto

National Institutes of Health, National Cancer Institute, Urologic Oncology Branch and Molecular Imaging Program, Bethesda, Maryland, United States

Jin Tae Kwak, Bradford J. Wood

National Institutes of Health, Clinical Center, Center for Interventional Oncology, Bethesda, Maryland, United States

J. Med. Imag. 4(2), 024506 (Jun 12, 2017). doi:10.1117/1.JMI.4.2.024506
History: Received March 4, 2017; Accepted May 12, 2017
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Abstract.  A prostate computer-aided diagnosis (CAD) based on random forest to detect prostate cancer using a combination of spatial, intensity, and texture features extracted from three sequences, T2W, ADC, and B2000 images, is proposed. The random forest training considers instance-level weighting for equal treatment of small and large cancerous lesions as well as small and large prostate backgrounds. Two other approaches, based on an AutoContext pipeline intended to make better use of sequence-specific patterns, were considered. One pipeline uses random forest on individual sequences while the other uses an image filter described to produce probability map-like images. These were compared to a previously published CAD approach based on support vector machine (SVM) evaluated on the same data. The random forest, features, sampling strategy, and instance-level weighting improve prostate cancer detection performance [area under the curve (AUC) 0.93] in comparison to SVM (AUC 0.86) on the same test data. Using a simple image filtering technique as a first-stage detector to highlight likely regions of prostate cancer helps with learning stability over using a learning-based approach owing to visibility and ambiguity of annotations in each sequence.

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© 2017 Society of Photo-Optical Instrumentation Engineers

Citation

Nathan Lay ; Yohannes Tsehay ; Matthew D. Greer ; Baris Turkbey ; Jin Tae Kwak, et al.
"Detection of prostate cancer in multiparametric MRI using random forest with instance weighting", J. Med. Imag. 4(2), 024506 (Jun 12, 2017). ; http://dx.doi.org/10.1117/1.JMI.4.2.024506


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