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Deep learning based needle tracking in prostate fusion biopsy

Fusion of pre-operative Magnetic Resonance Imaging (MRI) and Trans-Rectal Ultrasound (TRUS) guided biopsy (Fusion Biopsy) has proven to be more effective as compared to cognitive biopsy for the detection of prostate cancer. The detection of the biopsy needle used during the Ultrasound procedure has multiple applications like reporting, repeat biopsy planning and planning therapy. Earlier methods to solve this problem have only used image processing techniques like Hough- Transform or Graph-Cut. These techniques lack robustness because only image-based solution cannot take care of the huge variability in the data as well as the problem of needle going out of plane. Recent deep learning (DL) based solutions for needle detection have high latency and does not exploit temporal information present in TRUS imaging. In this paper, we propose a method to automatically detect the short-lived needle triggers and its position using temporal context incorporated into a DL model termed as Samsung Multi-Decoder Network (S-MDNet). The proposed solution has been tested on 8 patients and yields high sensitivity (96%) and specificity (95%) for the detection of the needle trigger event.