title: In vivo MRI based prostate cancer localization with random forests and auto-context model.
abstract: Prostate cancer is one of the major causes of cancer death for men. Magnetic resonance (MR) imaging is being increasingly used as an important modality to localize prostate cancer. Therefore, localizing prostate cancer in MRI with automated detection methods has become an active area of research. Many methods have been proposed for this task. However, most of previous methods focused on identifying cancer only in the peripheral zone (PZ), or classifying suspicious cancer ROIs into benign tissue and cancer tissue. Few works have been done on developing a fully automatic method for cancer localization in the entire prostate region, including central gland (CG) and transition zone (TZ). In this paper, we propose a novel learning-based multi-source integration framework to directly localize prostate cancer regions from in vivo MRI. We employ random forests to effectively integrate features from multi-source images together for cancer localization. Here, multi-source images include initially the multi-parametric MRIs (i.e., T2, DWI, and dADC) and later also the iteratively-estimated and refined tissue probability map of prostate cancer. Experimental results on 26 real patient data show that our method can accurately localize cancerous sections. The higher section-based evaluation (SBE), combined with the ROC analysis result of individual patients, shows that the proposed method is promising for in vivo MRI based prostate cancer localization, which can be used for guiding prostate biopsy, targeting the tumor in focal therapy planning, triage and follow-up of patients with active surveillance, as well as the decision making in treatment selection. The common ROC analysis with the AUC value of 0.832 and also the ROI-based ROC analysis with the AUC value of 0.883 both illustrate the effectiveness of our proposed method.
authors.affiliation: School of Science, Nanjing University of Science and Technology, Jiangsu, China; Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, United States.
authors.forename: Chunjun
authors.initials: C
authors.lastname: Qian
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, United States.
authors.forename: Li
authors.initials: L
authors.lastname: Wang
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, United States.
authors.forename: Yaozong
authors.initials: Y
authors.lastname: Gao
authors.affiliation: Department of Radiology, Section of Urology, University of Chicago, Chicago, IL, United States.
authors.forename: Ambereen
authors.initials: A
authors.lastname: Yousuf
authors.affiliation: School of Science, Nanjing University of Science and Technology, Jiangsu, China.
authors.forename: Xiaoping
authors.initials: X
authors.lastname: Yang
authors.affiliation: Department of Radiology, Section of Urology, University of Chicago, Chicago, IL, United States.
authors.forename: Aytekin
authors.initials: A
authors.lastname: Oto
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, United States; Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea. Electronic address: dgshen@med.unc.edu.
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
mesh_terms: D019540:Area Under Curve; D001331:Automation; D001706:Biopsy; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D011467:Prostate / Q000000981:diagnostic imaging / Q000473:pathology; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D012372:ROC Curve; D014218:Triage
keywords: Auto-context model; Cancer localization; MRI segmentation; Prostate cancer; Random forests