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A Review of the Application of Deep Learning in Brachytherapy

DOI: 10.4236/oalib.1106589, PP. 1-9

Subject Areas: Psychiatry & Psychology

Keywords: Deep Learning, Brachytherapy, Machine Learning, Automation

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Objective The automation of brachytherapy is the direction of future development. This article retrospectively studied the application of deep learning in brachytherapy of cervical cancer and clarified the status quo of development. Method This survey reviewed the application of machine learning and deep learning in brachytherapy for cervical cancer in the past 10 years. The survey retrieved and reviewed electronic journal articles in scientific databases such as Google Scholar and IEEE. The three sets of keywords used 1) deep learning, brachytherapy, 2) machine learning, brachytherapy, 3) automation, brachytherapy. Results Through research on the application of deep learning in brachytherapy, it is found that the U-net model is basically based on convolutional neural networks or some attention mechanisms are added to it, and it is applied to brachytherapy of prostate or cervical cancer. The automatic segmentation and reconstruction of the mid-source applicator (interpolation needle), target area delineation, optimization in the treatment planning system and dose calculation have achieved good results, proving that deep learning can be applied to the clinical treatment of brachytherapy. Conclusion The research on the application of deep learning in brachytherapy confirmed that deep learning can effectively promote the development of brachytherapy.

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Hu, H. , Shao, Y. and Hu, S. (2020). A Review of the Application of Deep Learning in Brachytherapy. Open Access Library Journal, 7, e6589. doi:


[1]  李龙婕, 邓晓琴. 宫颈癌近距离放射治疗进展[J]. 大连医科大学学报, 2019, 41(3): 193-198.
[2]  王金花, 宋金维, 王建东. 人工智能在宫颈癌筛查中的研究进展[J]. 癌症进展, 2019, 17(13): 1503-1505.
[3]  Lee, J.H., Ha, E.J. and Kim, J.H. (2019) Application of Deep Learning to the Diagnosis of Cervical Lymph Node Metastasis from Thyroid Cancer with CT. European Radiology, 29, 5452-5457.
[4]  Doyle, L.A., Yondorf, M., Peng, C., Harrison, A.S. and Den, R.B. (2018) Process Mapping and Time Study to Improve Efficiency of New Procedure Implementation for High-Dose Rate Prostate Brachytherapy. Journal of Healthcare Quality, 40, 19-26.
[5]  Meyer, P., Noblet, V., Mazzara, C. and Lallement, A. (2018) Survey on Deep Learning for Radiotherapy. Computers in Biology and Medicine, 98, 126-146.
[6]  William, W., Ware, A., Basaza-Ejiri, A.H. and Obungoloch, J. (2018) A Review of Image Analysis and Machine Learning Techniques for Automated Cervical Cancer Screening from Pap-Smear Images. Computer Methods and Programs in Biomedicine, 164, 15-22.
[7]  Chen, J., Remulla, D., Nguyen, J.H., Aastha, D., Liu, Y., Dasgupta, P., Hung, A.J. (2019) Current Status of Artificial intelligence Applications in Urology and Their Potential to Influence Clinical Practice. BJU International, 124, 567-577.
[8]  Cunha, J.A.M., Flynn, R., Bélanger, C., et al. (2020) Brachytherapy Future Directions. Seminars in Radiation Oncology, 30, 94-106.
[9]  Tajbakhsh, N., Jeyaseelan, L., Li, Q., Chiang, J.N., Wu, Z.H. and Ding, X.W. (2020) Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation. Medical Image Analysis, 63, Article ID: 101693.
[10]  Allman, D., Reiter, A. and Bell, M.A.L. (2018) Photoacoustic Source Detection and Reflection Artifact Removal Enabled by Deep Learning. IEEE Transactions on Medical Imaging, 37, 1464-1477.
[11]  Huang, X., Wang, J., Tang, F., Zhong, T. and Zhang, Y. (2018) Metal Artifact Reduction on Cervical CT Images by Deep Residual Learning. BioMedical Engineering Online, 17, Article No. 175.
[12]  Karimi, D., Zeng, Q., Mathur, P., et al. (2019) Accurate and Robust Deep Learning-Based Segmentation of the Prostate Clinical Target Volume in Ultrasound Images. Medical Image Analysis, 57, 186-196.
[13]  Lei, Y., Tian, S., He, X., et al. (2019) Ultrasound Prostate Segmentation Based on Multidirectional Deeply Supervised V-Net. Medical Physics, 46, 3194-3206.
[14]  Orlando, N., Gillies, D.J, Gyacskov, I., Romagnoli, C., D’Souza, D. and Fenster, A. (2020) Automatic Prostate Segmentation Using Deep Learning on Clinically Diverse 3D Transrectal Ultrasound Images. Medical Physics, 47, 2413-2426.
[15]  秦楠楠, 薛旭东, 吴爱林, 等. 基于U-net卷积神经网络的宫颈癌临床靶区和危及器官自动勾画的研究[J]. 中国医学物理学杂志, 2020, 37(4): 524-528.
[16]  Gessert, N., Priegnitz, T., Saathoff, T., et al. (2019) Spatio-Temporal Deep Learning Models for Tip force Estimation during Needle Insertion. International Journal of Computer Assisted Radiology and Surgery, 14, 1485-1493.
[17]  Hrinivich, W.T., Morcos, M., Viswanathan, A. and Lee, J.H. (2019) Automatic Tandem and Ring Reconstruction Using MRI for Cervical Cancer Brachytherapy. Medical Physics, 46, 4324-4332.
[18]  Jung, H., Shen, C.Y., Gonzalez, Y., Albuquerque, K. and Jia, X. (2019) Deep-Learning Assisted Automatic Digitization of Interstitial Needles in 3D CT Image Based High Dose-Rate Brachytherapy of Gynecological Cancer. Physics in Medicine & Biology, 64, Article ID: 215003.
[19]  Jung, H., Gonzalez, Y., Shen, C., Klages, P. and Albuquerque, K. (2019) Deep Learning Assisted Automatic Digitization of Applicators in 3D CT Image-Based High-Dose-Rate Brachytherapy of Gynecological Cancer. Brachytherapy, 18, 841-851.
[20]  Zaffino, P., Pernelle, G., Mastmeyer, A., et al. (2019) Fully Automatic Catheter Segmentation in MRI with 3D Convolutional Neural Networks: Application to MRI-Guided Gynecologic Brachytherapy. Physics in Medicine & Biology, 64, Article ID: 165008.
[21]  Dai, X., Lei, Y., Zhang, Y., et al. (2020) Automatic Multi-Catheter Detection Using Deeply Supervised Convolutional Neural Network in MRI-Guided HDR Prostate Brachytherapy. Medical Physics.
[22]  Zhang, Y., Lei, Y., Qiu, R.L.J., et al. (2020) Multi-Needle Localization with Attention U-Net in US-Guided HDR Prostate Brachytherapy. Medical Physics.
[23]  Wang, F., Xing, L., Bagshaw, H., Buyyounouski, M. and Han, B. (2020) Deep Learning Applications in Automatic Needle Segmentation in Ultrasound-Guided Prostate Brachytherapy. Medical Physics.
[24]  Wang, X., Wang, P., Li, C., et al. (2018) An Automated Dose Verification Software for Brachytherapy. Journal of Contemporary Brachytherapy, 10, 478-482.
[25]  Morcos, M. and Enger, S.A. (2020) Monte Carlo Dosimetry Study of Novel Rotating MRI-Compatible Shielded Tandems for Intensity Modulated Cervix Brachytherapy. European Journal of Medical Physics, 71, 178-184.
[26]  Mao, X.M., Pineau, J., Keyes, R. and Enger, S.A. (2020) RapidBrachyDL: Rapid Radiation Dose Calculations in Brachytherapy via Deep Learning. International Journal of Radiation Oncology, Biology, Physics.
[27]  Siauw, T., Cunha, A., Atamtürk, A., Hsu, I.-C., Pouliot, J. and Goldberg, K. (2011) IPIP: A New Approach to Inverse Planning for HDR Brachytherapy by Directly Optimizing Dosimetric Indices. Medical Physics, 38, 4045-4051.
[28]  Guthier, C., Aschenbrenner, K.P., Buergy, D., et al. (2015) A New Optimization Method Using a Compressed Sensing Inspired Solver for Real-Time LDR-Brachytherapy Treatment Planning. Physics in Medicine and Biology, 60, 2179-2194.
[29]  Nicolae, A., Morton, G., Chung, H., et al. (2017) Evaluation of a Machine-Learning Algorithm for Treatment Planning in Prostate Low-Dose-Rate Brachytherapy. International Journal of Radiation Oncology, Biology, Physics, 97, 822-829.
[30]  Shen, C., Gonzalez, Y., Klages, P., et al. (2019) Intelligent Inverse Treatment Planning via Deep Reinforcement Learning, a Proof-of-Principle Study in High Dose-Rate Brachytherapy for Cervical Cancer. Physics in Medicine & Biology, 64, Article ID: 115013.
[31]  Golshan, M., Karimi, D., Mahdavi, S., et al. (2020) Automatic Detection of Brachytherapy Seeds in 3D Ultrasound Images Using a Convolutional Neural Network. Physics in Medicine & Biology, 65, Article ID: 35016.
[32]  Nicolae, A., Semple, M., Lu, L., et al. (2020) Conventional vs. Machine Learning-Based Treatment Planning in Prostate Brachytherapy: Results of a Phase I Randomized Controlled Trial. Brachytherapy, 19, 470-476.
[33]  Tian, Z., Yen, A., Zhou, Z., et al. (2019) A Machine-Learning-Based Prediction Model of Fistula Formation after Interstitial Brachytherapy for Locally Advanced Gynecological Malignancies. Brachytherapy, 18, 530-538.
[34]  Zhen, X., Chen, J.W., Zhong, Z.C., Hrycushko, B., Zhou, L.H., Jiang, S., Albuquerque, K. and Gu, X.J. (2017) Deep Convolutional Neural Network with Transfer Learning for Rectum Toxicity Prediction in Cervical Cancer Radiotherapy: A Feasibility Study. Physics in Medicine & Biology, 62, 8246-8263.


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