Rigid and deformable corrections in real-time using deep learning for prostate fusion biopsy

Aditya Bhardwaj; Jun-Sung Park; Soumik Mukhopadhyay; Sikander Sharda; Yuri Son; Bhavya Ajani; Srinivas Rao Kudavelly

doi:10.1117/12.2548589

16 March 2020 Rigid and deformable corrections in real-time using deep learning for prostate fusion biopsy

Aditya Bhardwaj, Jun-Sung Park, Soumik Mukhopadhyay, Sikander Sharda, Yuri Son, Bhavya Ajani, Srinivas Rao Kudavelly

Proceedings Volume 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling; 113151W (2020) https://doi.org/10.1117/12.2548589
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Fusion biopsy reduces false negative rates in prostatic cancer detection compare to systemic biopsy. However, accuracy in biopsy sampling depends upon quality of alignment between pre-operative 3D MR and intra-operative 2D US. During live biopsy, the US-MR alignment may be disturbed due to prostate or patient rigid motion. Further, prostate gland deform due to probe pressure, which add error in biopsy sampling. In this paper, we describe a method for real-time 2D-3D multimodal registration, utilizing deep learning, to correct for rigid and deformable errors. Our method do not require an intermediate 3D US and works in real-time with an average runtime of 112 ms for both rigid and deformable corrections. On 12 patient data, our method reduces mean trans-registration error (TRE) from 8.890±5.106 mm to 2.988±1.513 mm, comparable to other state of the arts in accuracy.

Citation Download Citation

Aditya Bhardwaj, Jun-Sung Park, Soumik Mukhopadhyay, Sikander Sharda, Yuri Son, Bhavya Ajani, and Srinivas Rao Kudavelly "Rigid and deformable corrections in real-time using deep learning for prostate fusion biopsy", Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 113151W (16 March 2020); https://doi.org/10.1117/12.2548589

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