A deep learning method for real-time intraoperative US image segmentation in prostate brachytherapy-Reference-Cited by-同舟云学术

A deep learning method for real-time intraoperative US image segmentation in prostate brachytherapy

Published:2020-07-20 Issue:9 Volume:15 Page:1467-1476
ISSN:1861-6410
Container-title:International Journal of Computer Assisted Radiology and Surgery
language:en
Short-container-title:Int J CARS

Author:

Girum Kibrom Berihu^ORCID,Lalande Alain,Hussain Raabid,Créhange Gilles

Publisher

Springer Science and Business Media LLC

Subject

Health Informatics,Radiology Nuclear Medicine and imaging,General Medicine,Surgery,Computer Graphics and Computer-Aided Design,Computer Science Applications,Computer Vision and Pattern Recognition,Biomedical Engineering

Link

http://link.springer.com/content/pdf/10.1007/s11548-020-02231-x.pdf

Reference22 articles.

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2. Girum KB, Lalande A, Quivrin M, Bessières I, Pierrat N, Martin E, Cormier L, Petitfils A, Cosset JM, Créhange G (2018) Inferring postimplant dose distribution of salvage permanent prostate implant (PPI) after primary PPI on CT images. Brachytherapy 17:866–873. https://doi.org/10.1016/j.brachy.2018.07.017

3. Zelefsky MJ, Cohen GN, Taggar AS, Kollmeier M, McBride S, Mageras G, Zaider M (2017) Real-time intraoperative evaluation of implant quality and dose correction during prostate brachytherapy consistently improves target coverage using a novel image fusion and optimization program. Pract Radiat Oncol 7:319–324. https://doi.org/10.1016/j.prro.2017.01.009

4. Jaouen V, Bert J, Mountris KA, Boussion N, Schick U, Pradier O, Valeri A, Visvikis D (2019) Prostate volume segmentation in TRUS using hybrid edge-Bhattacharyya active surfaces. IEEE Trans Biomed Eng 66:920–933. https://doi.org/10.1109/TBME.2018.2865428

5. Karimi D, Zeng Q, Mathur P, Avinash A, Mahdavi S, Spadinger I, Abolmaesumi P, Salcudean SE (2019) Accurate and robust deep learning-based segmentation of the prostate clinical target volume in ultrasound images. Med Image Anal 57:186–196. https://doi.org/10.1016/j.media.2019.07.005

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