Skip to Main Content
With more than 110.000 new cases/year in Europe, prostate cancer (PCa) is one of the most frequent neoplasy. When suspects arise from standard diagnostic methods (i.e. Digital Rectal Exam, Transrectal Ultrasonography (TRUS), PSA level) a prostate biopsy (PBx) is mandatory. As patient discomfort and adverse event probability both grows with core number, it is desirable to reduce the number of PBx cores without negative impinging on diagnose accuracy. The work describes an innovative processing technique called real-time Computer Aided Biopsy (rtCAB) which enhances TRUS video stream with a false color overlay image, and suggests the physician where to sample thus reducing the total number of cores. Our proposal consists in a real-time non-linear classifier which processes the output of an original Maximum Likelihood estimator of Nakagami parameters based on Pade Approximant. The resulting algorithm, implemented making full use of CUDA parallel processing capabilities, is capable to deliver frame rates as high as 30 fps. Classification model was trained on a prostate gland adenocarcinoma database (400 PBx cores, 8000 ROIs). Ground truth for each core was established by an expert physician, providing tissue description and illness percentage for each core. The system was tuned for reducing the number of false positives while preserving an acceptable number of false negatives. Comparing to a classical double sextant PBx, the positive prediction value (PPV) of our method is 65% better, with an overall sensitivity of 100%.