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Automatic delineation of rectal cancer target volume and organs at risk based on convolutional neural network
Xia Xiang, Wang Jiazhou, Yang Lifeng, Zhang Zhen, Hu Weigang
Department of Radiotherapy, Cancer Hospital, Fudan University;Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
AbstractObjective To realize automatic delineation of rectal cancer target volume and normal tissues and improve clinical work efficiency. Methods The deep learning method based on convolutional neural network was adopted to construct neural network, learn and realize automatic delineation, and compare the differences between automatic delineation and manual delineation. Results Two hundred and ten caseswith rectal cancer were randomly assigned to a training set of 190 and a validation set of 20. The complete delineation of a single case took about 10s;the average Dice of CTV was 0.87±0.04;the average Dice of other normal tissues was bigger than 0.8;the Hausdorff distance (HD) index of CTV was 25.33±16.05;the mean distance to agreement (MDA) index was 3.07±1.49, and the Jaccard similarity coefficient (JSC) index was 0.77±0.07. Conclusion The deep learning method based on full convolutional neural network can realize the automatic delineation of rectal cancer target volume and improve work efficiency.
Corresponding Authors:
Wang Jiazhou, Email:wjiazhou@126.com
Cite this article:
Xia Xiang,Wang Jiazhou,Yang Lifeng et al. Automatic delineation of rectal cancer target volume and organs at risk based on convolutional neural network[J]. Chinese Journal of Radiation Oncology, 2020, 29(5): 374-368.
Xia Xiang,Wang Jiazhou,Yang Lifeng et al. Automatic delineation of rectal cancer target volume and organs at risk based on convolutional neural network[J]. Chinese Journal of Radiation Oncology, 2020, 29(5): 374-368.
[1] Mackie TR, Kapatoes J, Ruchala K, et al. Image guidance for precise conformal radiotherapy[J]. Int J Radiat Oncol Biol Phys, 2003, 56(1):89-105. DOI:10.1016/S0360-3016(03)00090-7.
[2] 阴晓娟,胡彩容,张秀春,等. 基于图谱库的ABAS自动勾画软件在头颈部肿瘤中的可行性研究[J]. 中华放射肿瘤学杂志, 2016, 25(11):1233-1237. DOI:10.3760/cma.j.issn.1004-4221.2016.11.019.
Yin XJ, Hu CR, Zhang XC, et al. Feasibility study of Abas automatic sketching software based on atlas in head and neck tumor[J]. Chin J Radiat Oncol, 2016, 25(11):1233-1237. DOI:10.3760/cma.j.issn.1004-4221.2016.11.019.
[3] Gambacorta MA, Valentini C, Dinapoli N, et al. Clinical validation of atlas-based auto-segmentation of pelvic volumes and normal tissue in rectal tumors using auto-segmentation computed system[J]. Acta Oncol, 2013, 52(8):1676-1681. DOI:10.3109/0284186X.2012.754989
[4] Anders LC, Stieler F, Siebenlist K, et al. Performance of an atlas-based autosegmentation software for delineation of target volumes for radiotherapy of breast and anorectal cancer[J]. Radiothe Oncol, 2012, 102(1):68-73. DOI:10.1016/j.radonc.2011.08.043.
[5] Men K, Dai J, Li Y. Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks[J]. Med Phys, 2017, 44(12):6377-6389. DOI:10.1002/mp.12602.
[6] Larsson R, Xiong JF, Song Y, et al. Automatic delineation of the clinical target volume in rectal cancer for radiation therapy using three-dimensional fully convolutional neural networks[C]//IEEE.2018 40th annual international conference of the IEEE engineering in medicine and biology society (EMBC). Honolulu, Hawaii:IEEE, 2018:5898-5901. DOI:10.1109/EMBC.2018.8513506
[7] Roels S, Duthoy W, Haustermans K, et al. Definition and delineation of the clinical target volume for rectal cancer[J]. Int J Radiat Oncol Biol Phys, 2006, 65(4):1129-1142. DOI:10.1016/j.ijrobp.2006.02.050.
[8] 唐源,金晶,朱远,等. 直肠癌术前/术后适形/调强放疗靶区勾画共识与图谱[J]. 中华放射肿瘤学杂志, 2018, 27(3):227-234. DOI:10.3760/cma.j.issn.1004-4221.2018.03.001.
Tang Y, Jin J, Zhu Y, et al. Consensus and map of target mapping of conformal/ IMRT for rectal cancer before/ after operation[J]. Chin J Radiat Oncol, 2018, 27(3):227-234. DOI:10.3760/cma.j.issn.1004-4221.2018.03.001.
[9] Wang J, Lu J, Qin G, et al. A deep learning-based autosegmentation of rectal tumors in MR images[J]. Med Phys, 2018, 45(6):2560-2564. DOI:10.1002/mp.12918.
[10] Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Trans Pattern Anal Mach Intel, 2016, 39(4):1-1. DOI:10.1109/TPAMI.2016.2572683.
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