An Approach for Live Motion Correction for TRUS-MR Prostate Fusion Biopsy using Deep Learning.

TRUS-MR fusion guided biopsy highly depends on the quality of alignment between pre-operative Magnetic Resonance (MR) image and live trans-rectal ultrasound (TRUS) image during biopsy. Lot of factors influence the alignment of prostate during the biopsy like rigid motion due to patient movement and deformation of the prostate due to probe pressure. For MR-TRUS alignment during live procedure, the efficiency of the algorithm and accuracy plays an important role.

A deep learning framework for supporting the classification of breast lesions in ultrasound images.

In this research, we exploited the deep learning framework to differentiate the distinctive types of lesions and nodules in breast acquired with ultrasound imaging. A biopsy-proven benchmarking dataset was built from 5151 patients cases containing a total of 7408 ultrasound breast images, representative of semi-automatically segmented lesions associated with masses. The dataset comprised 4254 benign and 3154 malignant lesions.