Geert J. S. Litjens

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Prostate MRI image segmentation has been an area of intense research due to the increased use of MRI as a modality for the clinical workup of prostate cancer. Segmentation is useful for various tasks, e.g. to accurately localize prostate boundaries for radiotherapy or to initialize multi-modal registration algorithms. In the past, it has been difficult for(More)
Zonal segmentation of the prostate into the central gland and peripheral zone is a useful tool in computer-aided detection of prostate cancer, because occurrence and characteristics of cancer in both zones differ substantially. In this paper we present a pattern recognition approach to segment the prostate zones. It incorporates three types of features that(More)
PURPOSE To retrospectively assess the use of the European Society of Urogenital Radiology (ESUR) Prostate Imaging Reporting and Data System (PI-RADS) criteria and 3-T multiparametric magnetic resonance (MR) imaging for detection of extraprostatic extension (EPE) of prostate cancer. MATERIALS AND METHODS The institutional review board approval requirement(More)
Prostate cancer is one of the major causes of cancer death for men in the western world. Magnetic resonance imaging (MRI) is being increasingly used as a modality to detect prostate cancer. Therefore, computer-aided detection of prostate cancer in MRI images has become an active area of research. In this paper we investigate a fully automated computer-aided(More)
Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for(More)
Prostate segmentation is an important and often mandatory step for several tasks, for example volume estimation, radiotherapy planning and computer-aided detection of prostate cancer. In this paper we evaluate a multi-atlas segmentation technique to segment the prostate from transversal T2-weighted MR images on data from the Prostate MR Image Segmentation(More)
BACKGROUND A challenge in the diagnosis of prostate cancer (PCa) is the accurate assessment of aggressiveness. OBJECTIVE To validate the performance of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) of the prostate at 3 tesla (T) for the assessment of PCa aggressiveness, with prostatectomy specimens as the reference standard. DESIGN,(More)
PURPOSE To determine if prostatitis and prostate cancer (PCa) can be distinguished by using apparent diffusion coefficients (ADCs) on magnetic resonance (MR) images, with specimens obtained at MR-guided biopsy as the standard of reference. MATERIALS AND METHODS The need for institutional review board approval and informed consent was waived. MR-guided(More)
PURPOSE To determine the best features to discriminate prostate cancer from benign disease and its relationship to benign disease class and cancer grade. MATERIALS AND METHODS The institutional review board approved this study and waived the need for informed consent. A retrospective cohort of 70 patients (age range, 48-70 years; median, 62 years), all of(More)
Pathologists face a substantial increase in workload and complexity of histopathologic cancer diagnosis due to the advent of personalized medicine. Therefore, diagnostic protocols have to focus equally on efficiency and accuracy. In this paper we introduce 'deep learning' as a technique to improve the objectivity and efficiency of histopathologic slide(More)