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Deep Learning-Based Seminal Vesicle and Vas Deferens Recognition in the Posterior Approach of Robot-Assisted Radical Prostatectomy.
Urology. 2023 Mar; 173:98-103.U

Abstract

OBJECTIVE

To develop a convolutional neural network to recognize the seminal vesicle and vas deferens (SV-VD) in the posterior approach of robot-assisted radical prostatectomy (RARP) and assess the performance of the convolutional neural network model under clinically relevant conditions.

METHODS

Intraoperative videos of robot-assisted radical prostatectomy performed by the posterior approach from 3 institutions were obtained between 2019 and 2020. Using SV-VD dissection videos, semantic segmentation of the seminal vesicle-vas deferens area was performed using a convolutional neural network-based approach. The dataset was split into training and test data in a 10:3 ratio. The average time required by 6 novice urologists to correctly recognize the SV-VD was compared using intraoperative videos with and without segmentation masks generated by the convolutional neural network model, which was evaluated with the test data using the Dice similarity coefficient. Training and test datasets were compared using the Mann-Whitney U-test and chi-square test. Time required to recognize the SV-VD was evaluated using the Mann-Whitney U-test.

RESULTS

From 26 patient videos, 1 040 images were created (520 SV-VD annotated images and 520 SV-VD non-displayed images). The convolutional neural network model had a Dice similarity coefficient value of 0.73 in the test data. Compared with original videos, videos with the generated segmentation mask promoted significantly faster seminal vesicle and vas deferens recognition (P < .001).

CONCLUSION

The convolutional neural network model provides accurate recognition of the SV-VD in the posterior approach RARP, which may be helpful, especially for novice urologists.

Links

Publisher Full Text (DOI)

Authors+Show Affiliations

Takeshita N
Surgical Device Innovation Office, National Cancer Center Hospital East, Kashiwa, Chiba, Japan; Department of Urology, Graduate School of Medicine, Chiba University, Chuo-ku, Chiba, Japan; Department of Urology, National Cancer Center Hospital East, Kashiwa, Chiba, Japan.
Sakamoto S
Department of Urology, Graduate School of Medicine, Chiba University, Chuo-ku, Chiba, Japan.
Kitaguchi D
Surgical Device Innovation Office, National Cancer Center Hospital East, Kashiwa, Chiba, Japan.
Takeshita N
Surgical Device Innovation Office, National Cancer Center Hospital East, Kashiwa, Chiba, Japan.
Yajima S
Department of Urology, National Cancer Center Hospital East, Kashiwa, Chiba, Japan.
Koike T
Surgical Device Innovation Office, National Cancer Center Hospital East, Kashiwa, Chiba, Japan.
Ishikawa Y
Surgical Device Innovation Office, National Cancer Center Hospital East, Kashiwa, Chiba, Japan.
Matsuzaki H
Surgical Device Innovation Office, National Cancer Center Hospital East, Kashiwa, Chiba, Japan.
Mori K
Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Japan.
Masuda H
Department of Urology, National Cancer Center Hospital East, Kashiwa, Chiba, Japan.
Ichikawa T
Department of Urology, Graduate School of Medicine, Chiba University, Chuo-ku, Chiba, Japan.
Ito M
Surgical Device Innovation Office, National Cancer Center Hospital East, Kashiwa, Chiba, Japan. Electronic address: maito@east.ncc.go.jp.

MeSH

MaleHumansSeminal VesiclesDeep LearningVas DeferensRoboticsProstatectomyImage Processing, Computer-Assisted

Pub Type(s)

Journal Article

Language

eng

PubMed ID

36572225

Citation

Takeshita, Nobushige, et al. "Deep Learning-Based Seminal Vesicle and Vas Deferens Recognition in the Posterior Approach of Robot-Assisted Radical Prostatectomy." Urology, vol. 173, 2023, pp. 98-103.
Takeshita N, Sakamoto S, Kitaguchi D, et al. Deep Learning-Based Seminal Vesicle and Vas Deferens Recognition in the Posterior Approach of Robot-Assisted Radical Prostatectomy. Urology. 2023;173:98-103.
Takeshita, N., Sakamoto, S., Kitaguchi, D., Takeshita, N., Yajima, S., Koike, T., Ishikawa, Y., Matsuzaki, H., Mori, K., Masuda, H., Ichikawa, T., & Ito, M. (2023). Deep Learning-Based Seminal Vesicle and Vas Deferens Recognition in the Posterior Approach of Robot-Assisted Radical Prostatectomy. Urology, 173, 98-103. https://doi.org/10.1016/j.urology.2022.12.006
Takeshita N, et al. Deep Learning-Based Seminal Vesicle and Vas Deferens Recognition in the Posterior Approach of Robot-Assisted Radical Prostatectomy. Urology. 2023;173:98-103. PubMed PMID: 36572225.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Deep Learning-Based Seminal Vesicle and Vas Deferens Recognition in the Posterior Approach of Robot-Assisted Radical Prostatectomy. AU - Takeshita,Nobushige, AU - Sakamoto,Shinichi, AU - Kitaguchi,Daichi, AU - Takeshita,Nobuyoshi, AU - Yajima,Shugo, AU - Koike,Tatsuki, AU - Ishikawa,Yuto, AU - Matsuzaki,Hiroki, AU - Mori,Kensaku, AU - Masuda,Hitoshi, AU - Ichikawa,Tomohiko, AU - Ito,Masaaki, Y1 - 2022/12/23/ PY - 2022/09/01/received PY - 2022/11/24/revised PY - 2022/12/04/accepted PY - 2022/12/27/pubmed PY - 2022/12/27/medline PY - 2022/12/26/entrez SP - 98 EP - 103 JF - Urology JO - Urology VL - 173 N2 - OBJECTIVE: To develop a convolutional neural network to recognize the seminal vesicle and vas deferens (SV-VD) in the posterior approach of robot-assisted radical prostatectomy (RARP) and assess the performance of the convolutional neural network model under clinically relevant conditions. METHODS: Intraoperative videos of robot-assisted radical prostatectomy performed by the posterior approach from 3 institutions were obtained between 2019 and 2020. Using SV-VD dissection videos, semantic segmentation of the seminal vesicle-vas deferens area was performed using a convolutional neural network-based approach. The dataset was split into training and test data in a 10:3 ratio. The average time required by 6 novice urologists to correctly recognize the SV-VD was compared using intraoperative videos with and without segmentation masks generated by the convolutional neural network model, which was evaluated with the test data using the Dice similarity coefficient. Training and test datasets were compared using the Mann-Whitney U-test and chi-square test. Time required to recognize the SV-VD was evaluated using the Mann-Whitney U-test. RESULTS: From 26 patient videos, 1 040 images were created (520 SV-VD annotated images and 520 SV-VD non-displayed images). The convolutional neural network model had a Dice similarity coefficient value of 0.73 in the test data. Compared with original videos, videos with the generated segmentation mask promoted significantly faster seminal vesicle and vas deferens recognition (P < .001). CONCLUSION: The convolutional neural network model provides accurate recognition of the SV-VD in the posterior approach RARP, which may be helpful, especially for novice urologists. SN - 1527-9995 UR - https://www.unboundmedicine.com/medline/citation/36572225/Deep_Learning_Based_Seminal_Vesicle_and_Vas_Deferens_Recognition_in_the_Posterior_Approach_of_Robot_Assisted_Radical_Prostatectomy_ DB - PRIME DP - Unbound Medicine ER -