期刊文献+

基于图谱库的自动轮廓勾画软件(ABAS)在鼻咽癌调强放疗中的应用 被引量:14

Clinical Evaluation of Atlas-based Autosegementation(ABAS)in NPC Intensity-Modulated Radiotherapy
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摘要 目的:评估基于患者图谱库的自动轮廓勾画软件(ABAS)在鼻咽癌调强计划中勾画危及器官的准确性及临床应用效果。方法:选取已使用调强治疗的10例鼻咽癌患者,将每例患者的定位CT图像及医生手动勾画的调强计划中关注的13种(20个)危及器官传人ABAS软件作为数据库模板。本研究选取22例新患者应用ABAS进行自动勾画.其结果传回计划系统并由各主管医生在此基础上进行手动修改。对ABAS自动勾画结果与医生在此基础上修改后的结果进行比较,并对使用时间进行统计。结果:ABAS自动勾画与医生修改勾画的形状相似性系数(DSC)平均值超过0.9的为下颌骨(0.97±0.02),颞叶(左0.97±0.03,右0.97±0.03),脑干(0.94±0.04),喉(0.94±0.06),脊髓(0.93±0.05),腮腺(左0.91±0.07,右0.91±0.08),眼球(左0.90±0.13,右0.96±0.13);DSC系数低于0.9的为颞颌关节(左0.89±0.10,右0.87±0.14),内耳(左O.86±0.14,右0.83±0.16),垂体(0.83±0.10),视交叉(0.70±0.14),晶体(左0.69±0.11,右0.70±0.14),视神经(左0.65±0.25。右0.64±0.14)。使用ABAS自动产生所有危及器官的时间约10分钟/每患者;医生在自动勾画基础上修改时间为6~25min/每患者,平均约15min/每患者。以前完全手动勾画以上危及器官估计需90min到120min,使用ABAS自动勾画则明显的缩短了时间。结论:基于患者图谱库的ABAS自动勾画软件,对目前鼻咽癌患者调强计划关注的大部分危及器官能够达到满意的自动勾画结果,临床应用表明能明显的节约医生勾画时间。 Objective: To evaluateauto-contouring accuracy and clinical efficiency using atlas-based auto-segmentation (ABAS) of CT images for nasopharyngeal (NPC)patient in intensity-modulated radiotherapy (IMRT). Methods: The images from 10 NPC patients treated with IMRT were selected as atlases input for ABAS, which including 20 organs-at-risk (OARs) contoured by doctors. For 22 clinical new patients, theauto contours generated by ABAS were compared with that edited auto contours by doctors. Dice similarity coeff±cients (DSC) were calculated between the auto contours and edited autocontours. The editing times were recorded. In addition, doctors conducted a subjective evaluation about the editing degree for each OAR. Results: The mean DSC greater than 0.9 were mandible (0.97+0.02), temporal lobe (Left (L) 0.97±0.03, Right(R) 0.97±0.03), brainstem (0.94±0.04), larynx (0.94±0.06), spinalcord (0.93±0.05),parotid (L 0.91 ±0.07, R 0.91±0.08), eye (L 0.90±0.13, R 0.96±0.13). DSC less than 0.9 were temporomandibular joint (L 0.89±0.10, R 0.87±0.14), inner ear (L 0.86±0.14, R 0.83 ±0.16), pituitary (0.83±0.10), optic chiasm (0.70±0.14), Lens (L 0.69±0.11, R 0.70±0.14), optic nerve (L 0.65±0.25, R 0.64±0.14). The time to auto-segment all OARs using ABAS was about 10 minutes per patient. Theediting time ranged from 6 to 25 minutes per patient, and the mean editing time was 15 minutes. Comparing the fully manual contour time of 90 to 120 minutes,ABAS auto-contour significantly shorten the time.Conclusions: Automatic segmentation generates contours of sufficient accuracy for most OARs in clinical NPC IMRT plan. The clinic.al application shows thatsubstantial time saving was achieved using editing, instead of manual contouring (15 vs.〉90min). In combination with other methods to improve automatic segmentation accuracy and broad applicability is under further study.
出处 《中国医学物理学杂志》 CSCD 2013年第2期3997-4000,4035,共5页 Chinese Journal of Medical Physics
关键词 ABAS自动勾画软件 危及器官 鼻咽癌 调强放疗 Atlas-based auto-segmentation Organs-at-risk NPC IMRT
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参考文献13

  • 1Chao KS,Ozyigit G,Tran BN,等.头颈部肿瘤术后调强放疗患者失败的模式[J].国际放射肿瘤学生物物理杂志,2003,55:312-321.
  • 2Dawson LA,Anzai Y,Marsh L,等.头颈肿瘤保护腮腺的适形和分段调强治疗的局部复发模式[J].国际放射肿瘤学生物物理杂志,2000,46:1117-1126.
  • 3Huang D,Xia P,Akazawa P,等.鼻窦癌调强和三维适形治疗计划的比较[J].国际放射肿瘤学生物物理杂志,2003,56:158-168.
  • 4Lee N,Xia P,Fischbein NJ,等.头颈肿瘤的调强放疗:UCSF靶区定义经验[J].国际放射肿瘤学生物物理杂志,2003,57:49-60.
  • 5Hong TS,Tome WA,Chappell RJ,等.头颈靶区定义的偏差:国际多中心研究[J].国际放射肿瘤学生物物理杂志,2004,60:S157-S158.
  • 6Steenbakkers R,Duppen J,Fitton I,等.鼻咽癌放疗靶区定义的差异:一种3-D分析方法[J].国际放射肿瘤学生物物理杂志,2004,60:S160-S161.
  • 7Jeanneret-Sozzi W.M0eckli R,Valley JF,等.靶区定义偏差的原因:SASRO在头颈和前列腺肿瘤的研究[J].放射治疗肿瘤,2006,182:450-457.
  • 8Dawant. BM, Hartmann SL,等.使用相似性和自由形变结合的方法自动3D分割勾画头颈MR图像中的内部结构:第一部.在正常目标中的方法学及验证[J].IEEE医学成像,1999,18:909-916.
  • 9Zhang T,Chi L等.在线头颈CT图像的自动定义:在线的自适应放疗[J].国际放射肿瘤学生物物理杂志,2007,68:522-530.
  • 10Teguh DN,Levendag PC,等.基于图谱库的头颈部多靶区及正常组织(咀嚼/吞咽)结构的自动分割的临床验证[J].国际放射肿瘤学生物物理杂志,2011,8l:950-957.

二级参考文献4

  • 1Tsuji SY, Hwang A, Weinberg V, et al. Dosimetric evaluation of automatic segmentation for adaptive IMRT for head-and-neck cancer. Int J Radiat Oncol Biol Phys,2010,77:707-714.
  • 2Sims R, lsambert A, Gregoire V, et al. A pre-elinieal assessment of an atlas-based automatic segmentation tool Ibr the head and neck. Radlother Oncol,2009,93:474-478.
  • 3Teguh DN, Levendag PC, Voet PW, et al. Clinical validation of atlas-based auto-segmentation of multiple target volumes and normal tissue (Swallowing/Mastication) structures in the head and neck. Int J Radiat Oncol Biol Phys,2011 ,In press.
  • 4Stapleford LJ, Lawson JD, Perkins C et al. Evaluation of automatic atlas-based lymph node segmentation for head-and-neck cancer. Int J Radiat Oncol Biol Phys,2010 ,77 :959-966.

共引文献15

同被引文献69

  • 1黄生鹏,杨振,雷明军,徐宏,王学伟.CT模拟在适形调强放射治疗中的应用研究[J].中国医学工程,2005,13(3):284-286. 被引量:4
  • 2潘建基,洪金省,张瑜.鼻咽癌常规外照射致后组颅神经损伤的危险因素研究[J].中华放射医学与防护杂志,2006,26(5):490-493. 被引量:18
  • 3Huang D, Xia P, Akazawa P, et al. Comparison of treatment plans using intenslty-modulated radiotherapy and three- dimensional conformal radiotheray for paranasal sinus carcinoma [J]. lnt J RadiatOncolBiolPhys, 2003, 56(1): 158-168.
  • 4Steenbakkers R J, Duppen JC, Fitton I, et al. Observer variation in target volume delineation of lung cancer related to radiation oncologist-computer interaction : a 'Big Brother' evaluation [ J ]. Radiother Oncol, 2005, 77(2): 182-190.
  • 5Zhang T, Chi Y, Meldolesi E, et al. Automatic delineation of on-line head-and-neck computed tomography images: toward on- line adaptive radiotherapy [ J ]. Int J Radiat Oncol Biol Phys, 2007, 68(2) : 522-530. Teguh DN, Levendag PC, Voet PW, et al. Clinical validation of Atlas-based auto-segmention of muhiple.
  • 6target volumes and normal tissue (swallowing/mastication) structures in the head and neck[J]. IntJRadiatOncolBiolPhys, 2011, 81(4):950- 957.
  • 7Stapleford LJ, Lawson JD, Perkins C, et al. Evaluation of automatic atlas-based lymph node segmentation for head-and- neck cancer[J], lnt J Radiat Oncol Biol Phys, 2010, 77(3): 959-966.
  • 8Tsuji SY, Hwa,tg A, Weinberg V, et al. Dosimetric evaluation of automatic segmentation for adaptive IMRT for head and neck cancer[J]. Int J Radiat Oncol Biol Phys, 2010, 77 (3): 707-714.
  • 9La Macchia M, Fellin F, Amichetti M, et al. Systematic evaluation of three different commercial software solutions or automatic segmentation for adaptive therapy in head-and-neck, prostate and pleural cancer[J]. Radiat Oncol, 2012, 7 ( 1 ) : 160-176.
  • 10Zijdenbos AP, Dawant BM, Margolin RA, et al. Morphometric analysis of white matter lesions in MR images: method and validation[ J]. IEEE Trans Med Imaging, 1994, 13 (4): 716-724.

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