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Proceedings Paper

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

Paper Details

Date Published: 16 March 2020
PDF: 14 pages
Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 113151W (16 March 2020); doi: 10.1117/12.2548589
Show Author Affiliations
Aditya Bhardwaj, Samsung Research Institute, Bangalore, India (India)
Jun-Sung Park, SAMSUNG Electronics Co., Ltd. (Korea, Republic of)
Soumik Mukhopadhyay, Samsung Research Institute, Bangalore, India (India)
Sikander Sharda, Consultant (India)
Yuri Son, SAMSUNG Electronics Co., Ltd. (Korea, Republic of)
Bhavya Ajani, Samsung Research Institute, Bangalore, India (India)
Srinivas Rao Kudavelly, SAMSUNG R&D Institute India, Bangalore (India)


Published in SPIE Proceedings Vol. 11315:
Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling
Baowei Fei; Cristian A. Linte, Editor(s)

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