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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)
Active shape models (ASMs) and active appearance models (AAMs) are popular approaches for medical image segmentation that use shape information to drive the segmentation process. Both approaches rely on image derived landmarks (specified either manually or automatically) to define the object's shape, which require accurate triangulation and alignment. An(More)
PURPOSE To identify and evaluate textural quantitative imaging signatures (QISes) for tumors occurring within the central gland (CG) and peripheral zone (PZ) of the prostate, respectively, as seen on in vivo 3 Tesla (T) endorectal T2-weighted (T2w) MRI. MATERIALS AND METHODS This study used 22 preoperative prostate MRI data sets (16 PZ, 6 CG) acquired(More)
When rendering effects such as motion blur and defocus blur, shading can become very expensive if done in a naïve way, i.e. shading each visibility sample. To improve performance, previous work often decouple shading from visibility sampling using shader caching algorithms. We present a novel technique for reusing shading in a stochastic rasterizer. Shading(More)
CONTEXT Co-registration of ex-vivo histologic images with pre-operative imaging (e.g., magnetic resonance imaging [MRI]) can be used to align and map disease extent, and to identify quantitative imaging signatures. However, ex-vivo histology images are frequently sectioned into quarters prior to imaging. AIMS This work presents Histostitcher™, a software(More)
PURPOSE Prostate gland segmentation is a critical step in prostate radiotherapy planning, where dose plans are typically formulated on CT. Pretreatment MRI is now beginning to be acquired at several medical centers. Delineation of the prostate on MRI is acknowledged as being significantly simpler to perform, compared to delineation on CT. In this work, the(More)
Recently, high resolution 3 Tesla (T) Dynamic Contrast-Enhanced MRI (DCE-MRI) of the prostate has emerged as a promising modality for detecting prostate cancer (CaP). Computer-aided diagnosis (CAD) schemes for DCE-MRI data have thus far been primarily developed for breast cancer and typically involve model fitting of dynamic intensity changes as a function(More)
In this work we present an improvement to the popular Active Appearance Model (AAM) algorithm, that we call the Multiple-Levelset AAM (MLA). The MLA can simultaneously segment multiple objects, and makes use of multiple levelsets, rather than anatomical landmarks, to define the shapes. AAMs traditionally define the shape of each object using a set of(More)
Screening and detection of prostate cancer (CaP) currently lacks an image-based protocol which is reflected in the high false negative rates currently associated with blinded sextant biopsies. Multi-protocol magnetic resonance imaging (MRI) offers high resolution functional and structural data about internal body structures (such as the prostate). In this(More)
Segmentation of the prostate boundary on clinical images is useful in a large number of applications including calculation of prostate volume pre- and post-treatment, to detect extra-capsular spread, and for creating patient-specific anatomical models. Manual segmentation of the prostate boundary is, however, time consuming and subject to inter- and(More)