A partial augmented reality system with live ultrasound and registered preoperative MRI for guiding robot-assisted radical prostatectomy.

We propose an image guidance system for robot assisted laparoscopic radical prostatectomy (RALRP). A virtual 3D reconstruction of the surgery scene is displayed underneath the endoscope's feed on the surgeon's console. This scene consists of an annotated preoperative Magnetic Resonance Image (MRI) registered to intraoperative 3D Trans-rectal Ultrasound (TRUS) as well as real-time sagittal 2D TRUS images of the prostate, 3D models of the prostate, the surgical instrument and the TRUS transducer.

Automatic grading of prostate cancer in digitized histopathology images: Learning from multiple experts.

Prostate cancer (PCa) is a heterogeneous disease that is manifested in a diverse range of histologic patterns and its grading is therefore associated with an inter-observer variability among pathologists, which may lead to an under- or over-treatment of patients. In this work, we develop a computer aided diagnosis system for automatic grading of PCa in digitized histopathology images using supervised learning methods. Our pipeline comprises extraction of multi-scale features that include glandular, cellular, and image-based features.

Learning in data-limited multimodal scenarios: Scandent decision forests and tree-based features.

Incomplete and inconsistent datasets often pose difficulties in multimodal studies. We introduce the concept of scandent decision trees to tackle these difficulties. Scandent trees are decision trees that optimally mimic the partitioning of the data determined by another decision tree, and crucially, use only a subset of the feature set.