Automatic Segmentation of Clinical Target Volume and Organs-at-Risk for Breast Conservative Radiotherapy Using a Convolutional Neural Network

References

• Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin. 2020;70:7–30.31912902
   PubMed Web of Science ®Google Scholar
• Veronesi U, Cascinelli N, Mariani L, et al. Twenty-year follow-up of a randomized study comparing breast-conserving surgery with radical mastectomy for early breast cancer. N Engl J Med. 2002;347(16):1227–1232. doi:10.1056/NEJMoa02098912393819
   PubMed Web of Science ®Google Scholar
• Darby S, McGale P, Correa C, et al. Effect of radiotherapy after breast-conserving surgery on 10-year recurrence and 15-year breast cancer death: meta-analysis of individual patient data for 10,801 women in 17 randomised trials. Lancet. 2011;378:1707–1716.22019144
   PubMed Web of Science ®Google Scholar
• Andrianarison VA, Laouiti M, Fargier-Bochaton O, et al. Contouring workload in adjuvant breast cancer radiotherapy. Cancer Radiother. 2018;22:747–753. doi:10.1016/j.canrad.2018.01.00830322819
   PubMed Web of Science ®Google Scholar
• Jensen NK, Mulder D, Lock M, et al. Dynamic contrast enhanced CT aiding gross tumor volume delineation of liver tumors: an interobserver variability study. Radiother Oncol. 2014;111:153–157. doi:10.1016/j.radonc.2014.01.02624631143
   PubMed Web of Science ®Google Scholar
• Steenbergen P, Haustermans K, Lerut E, et al. Prostate tumor delineation using multiparametric magnetic resonance imaging: inter-observer variability and pathology validation. Radiother Oncol. 2015;115:186–190. doi:10.1016/j.radonc.2015.04.01225935742
   PubMed Web of Science ®Google Scholar
• Wong WKH, Leung LHT, Kwong DLW. Evaluation and optimization of the parameters used in multiple-atlas-based segmentation of prostate cancers in radiation therapy. Brit J Radiol. 2016;89. doi:10.1259/bjr.20140732
   Web of Science ®Google Scholar
• Tong N, Gou SP, Yang SY, Ruan D, Sheng K. Fully automatic multi-organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks. Med Phys. 2018;45:4558–4567. doi:10.1002/mp.1314730136285
   PubMed Web of Science ®Google Scholar
• Fortunati V, Verhaart RF, van der Lijn F, et al. Tissue segmentation of head and neck CT images for treatment planning: a multiatlas approach combined with intensity modeling. Med Phys. 2013;40:071905. doi:10.1118/1.481097123822442
   PubMed Web of Science ®Google Scholar
• Tao CJ, Yi JL, Chen NY, et al. Multi-subject atlas-based auto-segmentation reduces interobserver variation and improves dosimetric parameter consistency for organs at risk in nasopharyngeal carcinoma: a multi-institution clinical study. Radiother Oncol. 2015;115:407–411. doi:10.1016/j.radonc.2015.05.01226025546
   PubMed Web of Science ®Google Scholar
• Men K, Dai JR, Li YX. Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks. Med Phys. 2017;44:6377–6389. doi:10.1002/mp.1260228963779
   PubMed Web of Science ®Google Scholar
• Ibtehaz N, Rahman MS. MultiResUNet: rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural Netw. 2020;121:74–87. doi:10.1016/j.neunet.2019.08.02531536901
   PubMed Web of Science ®Google Scholar
• Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. Lect Notes Comput Sci. 2015;9351:234–241.
   Google Scholar
• Badrinarayanan V, Kendall A, Cipolla R. SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell. 2017;39:2481–2495. doi:10.1109/TPAMI.2016.264461528060704
   PubMed Web of Science ®Google Scholar
• Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell. 2017;39:640–651. doi:10.1109/TPAMI.2016.257268327244717
   PubMed Web of Science ®Google Scholar
• Men K, Zhang T, Chen XY, et al. Fully automatic and robust segmentation of the clinical target volume for radiotherapy of breast cancer using big data and deep learning. Phys Med. 2018;50:13–19. doi:10.1016/j.ejmp.2018.05.00629891089
   PubMed Web of Science ®Google Scholar
• Ibragimov B, Xing L. Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks. Med Phys. 2017;44:547–557. doi:10.1002/mp.1204528205307
   PubMed Web of Science ®Google Scholar
• Chan JW, Kearney V, Haaf S, et al. A convolutional neural network algorithm for automatic segmentation of head and neck organs at risk using deep lifelong learning. Med Phys. 2019;46:2204–2213. doi:10.1002/mp.1349530887523
   PubMed Web of Science ®Google Scholar
• Men K, Chen X, Zhang Y, et al. Deep deconvolutional neural network for target segmentation of nasopharyngeal cancer in planning computed tomography images. Front Oncol. 2017;7:315. doi:10.3389/fonc.2017.0031529376025
   PubMed Web of Science ®Google Scholar
• Liu Z, Liu X, Xiao B, et al. Segmentation of organs-at-risk in cervical cancer CT images with a convolutional neural network. Phys Med. 2020;69:184–191. doi:10.1016/j.ejmp.2019.12.00831918371
   PubMed Web of Science ®Google Scholar
• Offersen BV, Boersma LJ, Kirkove C, et al. ESTRO consensus guideline on target volume delineation for elective radiation therapy of early stage breast cancer. Radiother Oncol. 2015;114:3–10. doi:10.1016/j.radonc.2014.11.03025630428
   PubMed Web of Science ®Google Scholar
• R.T.O. Group, Breast Cancer Atlas for Radiation Therapy Planning: Consensus Definition; 2018.
   Google Scholar
• Dubey SR, Chakraborty S, Roy SK, Mukherjee S, Singh SK, Chaudhuri BB. diffGrad: an optimization method for convolutional neural networks. IEEE Trans Neural Netw Learn Syst. 2019;31(11):4500–4511. doi:10.1109/TNNLS.2019.2955777
   Web of Science ®Google Scholar
• Kalayeh MM, Shah M. Training faster by separating modes of variation in batch-normalized models. IEEE Trans Pattern Anal Mach Intell. 2020;42:1483–1500. doi:10.1109/TPAMI.2019.289578130703010
   PubMed Web of Science ®Google Scholar
• Lennerts E, Coucke P. [The radiotherapy journey: from information to treatment]. Rev Med Liege. 2014;69(Suppl 1):3–8. Romanian.
   PubMedGoogle Scholar
• Eldesoky AR, Yates ES, Nyeng TB, et al. Internal and external validation of an ESTRO delineation guideline - dependent automated segmentation tool for loco-regional radiation therapy of early breast cancer. Radiother Oncol. 2016;121:424–430. doi:10.1016/j.radonc.2016.09.00527697296
   PubMed Web of Science ®Google Scholar
• Jameson MG, Holloway LC, Vial PJ, Vinod SK, Metcalfe PE. A review of methods of analysis in contouring studies for radiation oncology. J Med Imaging Radiat Oncol. 2010;54:401–410. doi:10.1111/j.1754-9485.2010.02192.x20958937
   PubMed Web of Science ®Google Scholar
• Barillot I, Chauvet B, Hannoun Lévi JM, Lisbona A, Leroy T, Mahé MA. [The irradiation process]. Cancer Radiother. 2016;20(Suppl):S8–S19. French. doi:10.1016/j.canrad.2016.07.01327516048
   PubMedGoogle Scholar
• Lee LK, Liew SC, Thong WJ. A review of image segmentation methodologies in medical image. Lect Notes Electr Eng. 2015;315.
   Google Scholar
• Liu Z, Liu F, Chen W, et al. Automatic segmentation of clinical target volumes for post-modified radical mastectomy radiotherapy using convolutional neural networks. Front Oncol. 2021;10:581347.33665160
   PubMed Web of Science ®Google Scholar
• Gregucci F, Fozza A, Falivene S, et al; R. Italian Society of, G. Clinical oncology breast, present clinical practice of breast cancer radiotherapy in Italy: a nationwide survey by the Italian Society of Radiotherapy and Clinical Oncology (AIRO) Breast Group. Radiol Med. 2020;125(7):674–682. doi:10.1007/s11547-020-01147-532078120
   PubMed Web of Science ®Google Scholar
• Batumalai V, Koh ES, Delaney GP, et al. Interobserver variability in clinical target volume delineation in tangential breast irradiation: a comparison between radiation oncologists and radiation therapists. Clin Oncol. 2011;23:108–113. doi:10.1016/j.clon.2010.10.004
   PubMed Web of Science ®Google Scholar
• Mast M, Coerkamp E, Heijenbrok M, et al. Target volume delineation in breast conserving radiotherapy: are co-registered CT and MR images of added value? Radiat Oncol. 2014;9(1):65. doi:10.1186/1748-717X-9-6524571843
   PubMed Web of Science ®Google Scholar
• van der Leij F, Elkhuizen PHM, Janssen TM, et al. Target volume delineation in external beam partial breast irradiation: less inter-observer variation with preoperative- compared to postoperative delineation. Radiother Oncol. 2014;110:467–470. doi:10.1016/j.radonc.2013.10.03324262820
   PubMed Web of Science ®Google Scholar
• Balagopal A, Kazemifar S, Nguyen D, et al. Fully automated organ segmentation in male pelvic CT images. Phys Med Biol. 2018;63:245015. doi:10.1088/1361-6560/aaf11c30523973
   PubMed Web of Science ®Google Scholar