MRI/TRUS-Guided Auxiliary System for Transperineal Prostate Biopsy Based on Deep Learning

Bo Wang; Liang Yu; Zheng Pan; Hang Ni; Zhaopeng Lin; Yi Fan

doi:10.1109/ICICSP59554.2023.10390795

MRI/TRUS-Guided Auxiliary System for Transperineal Prostate Biopsy Based on Deep Learning

Bo Wang
, Liang Yu
, Zheng Pan
, Hang Ni
, Zhaopeng Lin
, Yi Fan

Ltd
Shanghai Jiao Tong University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Prostate cancer is prevalent cancer worldwide, ranking fourth in frequency. A systematic biopsy can significantly increase a patient's 5-year survival rate. However, accurate and effective biopsy based on pre-biopsy magnetic resonance imaging (MRI) and hand-held transrectal ultrasound (TRUS) is reliant on the operator's expertise. This paper presents a novel MRI/TRUS auxiliary system based on the deep learning method for prostate intervention, including the system architecture and workflow. The system comprises a preprocessing unit, a registration unit, and a navigation unit. The nnU-net is utilized to generate segmentation labels for the prostate and surrounding organs. The DeepReg network is used in the registration unit to fuse real-time TRUS and pre-biopsy MRI. The navigation unit provides reference insertion positions and alerts the operator of potential collisions and damage to nearby organs. The experimental results, based on data from both the TCIA and hospitals, demonstrate the effectiveness and feasibility of the auxiliary system.

Original language	English
Title of host publication	2023 6th International Conference on Information Communication and Signal Processing, ICICSP 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	914-921
Number of pages	8
ISBN (Electronic)	9798350339994
DOIs	https://doi.org/10.1109/ICICSP59554.2023.10390795
State	Published - 2023
Externally published	Yes
Event	6th International Conference on Information Communication and Signal Processing, ICICSP 2023 - Xi'an, China Duration: 23 Sep 2023 → 25 Sep 2023

Publication series

Name	2023 6th International Conference on Information Communication and Signal Processing, ICICSP 2023

Conference

Conference	6th International Conference on Information Communication and Signal Processing, ICICSP 2023
Country/Territory	China
City	Xi'an
Period	23/09/23 → 25/09/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

biopsy auxiliary system
deep learning
image recognition
MRI/TRUS registration
prostate biopsy

Access to Document

10.1109/ICICSP59554.2023.10390795

Cite this

Wang, B., Yu, L., Pan, Z., Ni, H., Lin, Z., & Fan, Y. (2023). MRI/TRUS-Guided Auxiliary System for Transperineal Prostate Biopsy Based on Deep Learning. In 2023 6th International Conference on Information Communication and Signal Processing, ICICSP 2023 (pp. 914-921). (2023 6th International Conference on Information Communication and Signal Processing, ICICSP 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICICSP59554.2023.10390795

Wang, Bo ; Yu, Liang ; Pan, Zheng et al. / MRI/TRUS-Guided Auxiliary System for Transperineal Prostate Biopsy Based on Deep Learning. 2023 6th International Conference on Information Communication and Signal Processing, ICICSP 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 914-921 (2023 6th International Conference on Information Communication and Signal Processing, ICICSP 2023).

@inproceedings{584b3d4dfab640c6ab7238dd42397bfd,

title = "MRI/TRUS-Guided Auxiliary System for Transperineal Prostate Biopsy Based on Deep Learning",

abstract = "Prostate cancer is prevalent cancer worldwide, ranking fourth in frequency. A systematic biopsy can significantly increase a patient's 5-year survival rate. However, accurate and effective biopsy based on pre-biopsy magnetic resonance imaging (MRI) and hand-held transrectal ultrasound (TRUS) is reliant on the operator's expertise. This paper presents a novel MRI/TRUS auxiliary system based on the deep learning method for prostate intervention, including the system architecture and workflow. The system comprises a preprocessing unit, a registration unit, and a navigation unit. The nnU-net is utilized to generate segmentation labels for the prostate and surrounding organs. The DeepReg network is used in the registration unit to fuse real-time TRUS and pre-biopsy MRI. The navigation unit provides reference insertion positions and alerts the operator of potential collisions and damage to nearby organs. The experimental results, based on data from both the TCIA and hospitals, demonstrate the effectiveness and feasibility of the auxiliary system.",

keywords = "biopsy auxiliary system, deep learning, image recognition, MRI/TRUS registration, prostate biopsy",

author = "Bo Wang and Liang Yu and Zheng Pan and Hang Ni and Zhaopeng Lin and Yi Fan",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 6th International Conference on Information Communication and Signal Processing, ICICSP 2023 ; Conference date: 23-09-2023 Through 25-09-2023",

year = "2023",

doi = "10.1109/ICICSP59554.2023.10390795",

language = "英语",

series = "2023 6th International Conference on Information Communication and Signal Processing, ICICSP 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "914--921",

booktitle = "2023 6th International Conference on Information Communication and Signal Processing, ICICSP 2023",

}

Wang, B, Yu, L, Pan, Z, Ni, H, Lin, Z & Fan, Y 2023, MRI/TRUS-Guided Auxiliary System for Transperineal Prostate Biopsy Based on Deep Learning. in 2023 6th International Conference on Information Communication and Signal Processing, ICICSP 2023. 2023 6th International Conference on Information Communication and Signal Processing, ICICSP 2023, Institute of Electrical and Electronics Engineers Inc., pp. 914-921, 6th International Conference on Information Communication and Signal Processing, ICICSP 2023, Xi'an, China, 23/09/23. https://doi.org/10.1109/ICICSP59554.2023.10390795

MRI/TRUS-Guided Auxiliary System for Transperineal Prostate Biopsy Based on Deep Learning. / Wang, Bo; Yu, Liang; Pan, Zheng et al.
2023 6th International Conference on Information Communication and Signal Processing, ICICSP 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 914-921 (2023 6th International Conference on Information Communication and Signal Processing, ICICSP 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - MRI/TRUS-Guided Auxiliary System for Transperineal Prostate Biopsy Based on Deep Learning

AU - Wang, Bo

AU - Yu, Liang

AU - Pan, Zheng

AU - Ni, Hang

AU - Lin, Zhaopeng

AU - Fan, Yi

PY - 2023

Y1 - 2023

N2 - Prostate cancer is prevalent cancer worldwide, ranking fourth in frequency. A systematic biopsy can significantly increase a patient's 5-year survival rate. However, accurate and effective biopsy based on pre-biopsy magnetic resonance imaging (MRI) and hand-held transrectal ultrasound (TRUS) is reliant on the operator's expertise. This paper presents a novel MRI/TRUS auxiliary system based on the deep learning method for prostate intervention, including the system architecture and workflow. The system comprises a preprocessing unit, a registration unit, and a navigation unit. The nnU-net is utilized to generate segmentation labels for the prostate and surrounding organs. The DeepReg network is used in the registration unit to fuse real-time TRUS and pre-biopsy MRI. The navigation unit provides reference insertion positions and alerts the operator of potential collisions and damage to nearby organs. The experimental results, based on data from both the TCIA and hospitals, demonstrate the effectiveness and feasibility of the auxiliary system.

AB - Prostate cancer is prevalent cancer worldwide, ranking fourth in frequency. A systematic biopsy can significantly increase a patient's 5-year survival rate. However, accurate and effective biopsy based on pre-biopsy magnetic resonance imaging (MRI) and hand-held transrectal ultrasound (TRUS) is reliant on the operator's expertise. This paper presents a novel MRI/TRUS auxiliary system based on the deep learning method for prostate intervention, including the system architecture and workflow. The system comprises a preprocessing unit, a registration unit, and a navigation unit. The nnU-net is utilized to generate segmentation labels for the prostate and surrounding organs. The DeepReg network is used in the registration unit to fuse real-time TRUS and pre-biopsy MRI. The navigation unit provides reference insertion positions and alerts the operator of potential collisions and damage to nearby organs. The experimental results, based on data from both the TCIA and hospitals, demonstrate the effectiveness and feasibility of the auxiliary system.

KW - biopsy auxiliary system

KW - deep learning

KW - image recognition

KW - MRI/TRUS registration

KW - prostate biopsy

UR - https://www.scopus.com/pages/publications/85184800490

U2 - 10.1109/ICICSP59554.2023.10390795

DO - 10.1109/ICICSP59554.2023.10390795

M3 - 会议稿件

AN - SCOPUS:85184800490

T3 - 2023 6th International Conference on Information Communication and Signal Processing, ICICSP 2023

SP - 914

EP - 921

BT - 2023 6th International Conference on Information Communication and Signal Processing, ICICSP 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 6th International Conference on Information Communication and Signal Processing, ICICSP 2023

Y2 - 23 September 2023 through 25 September 2023

ER -

Wang B, Yu L, Pan Z, Ni H, Lin Z, Fan Y. MRI/TRUS-Guided Auxiliary System for Transperineal Prostate Biopsy Based on Deep Learning. In 2023 6th International Conference on Information Communication and Signal Processing, ICICSP 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 914-921. (2023 6th International Conference on Information Communication and Signal Processing, ICICSP 2023). doi: 10.1109/ICICSP59554.2023.10390795

MRI/TRUS-Guided Auxiliary System for Transperineal Prostate Biopsy Based on Deep Learning

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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