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15 February 2021 Deep learning based needle tracking in prostate fusion biopsy
Soumik Mukhopadhyay, Praful Mathur, Aditya Bharadwaj, Yuri Son, Jun-Sung Park, Srinivas Rao Kudavelly, Sangha Song, Hokyung Kang
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Proceedings Volume 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling; 115982A (2021) https://doi.org/10.1117/12.2580891
Event: SPIE Medical Imaging, 2021, Online Only
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Abstract
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.
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Soumik Mukhopadhyay, Praful Mathur, Aditya Bharadwaj, Yuri Son, Jun-Sung Park, Srinivas Rao Kudavelly, Sangha Song, and Hokyung Kang "Deep learning based needle tracking in prostate fusion biopsy", Proc. SPIE 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling, 115982A (15 February 2021); https://doi.org/10.1117/12.2580891
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KEYWORDS
Biopsy
Prostate
Magnetic resonance imaging
Ultrasonography
Image processing
Prostate cancer
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