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Autosegmentation of cardiac substructures in respiratory-gated,non-contrasted computed tomography images
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作者 Mark Farrugia Han Yu +1 位作者 Anurag K Singh Harish Malhotra 《World Journal of Clinical Oncology》 CAS 2021年第2期95-102,共8页
BACKGROUND Radiation dose to specific cardiac substructures can have a significant on treatment related morbidity and mortality,yet definition of these structures is labor intensive and not standard.Autosegmentation s... BACKGROUND Radiation dose to specific cardiac substructures can have a significant on treatment related morbidity and mortality,yet definition of these structures is labor intensive and not standard.Autosegmentation software may potentially address these issues,however it is unclear whether this approach can be broadly applied across different treatment planning conditions.We investigated the feasibility of autosegmentation of the cardiac substructures in four-dimensional(4D)computed tomography(CT),respiratory-gated,non-contrasted imaging.AIM To determine whether autosegmentation can be successfully employed on 4DCT respiratory-gated,non-contrasted imaging.METHODS We included patients who underwent stereotactic body radiation therapy for inoperable,early-stage non-small cell lung cancer from 2007 to 2019.All patients were simulated via 4DCT imaging with respiratory gating without intravenous contrast.Generated structure quality was evaluated by degree of required manual edits and volume discrepancy between the autocontoured structures and its edited sister structure.RESULTS Initial 17-structure cardiac atlas was generated with 20 patients followed by three successive iterations of 10 patients using MIM software.The great vessels and heart chambers were reliably autosegmented with most edits considered minor.In contrast,coronary arteries either failed to be autosegmented or the generated structures required major alterations necessitating deletion and manual definition.Similarly,the generated mitral and tricuspid valves were poor whereas the aortic and pulmonary valves required at least minor and moderate changes respectively.For the majority of subsites,the additional samples did not appear to substantially impact the quality of generated structures.Volumetric analysis between autosegmented and its manually edited sister structure yielded comparable findings to the physician-based assessment of structure quality.CONCLUSION The use of MIM software with 30-sample subject library was found to be useful in delineatin 展开更多
关键词 Autosegmentation autocontouring Lung cancer Radiation therapy Heart substructures Stereotactic body radiation therapy
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基于参考图像的ABAS软件自动勾画技术在头颈部肿瘤中的应用研究 被引量:16
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作者 张秀春 胡彩容 +1 位作者 陈传本 蔡勇君 《中华放射肿瘤学杂志》 CSCD 北大核心 2011年第6期510-512,共3页
目的评估ABAS软件对头颈部肿瘤自适应调强放疗患者基于cT图像自动勾画靶区与危及器官轮廓的吻合度。方法对随机抽取的10例已勾画轮廓的头颈部肿瘤患者设计两种形变自动勾画方式,第1种对每例患者进行基于各自CT图像的形变自动勾画,第2... 目的评估ABAS软件对头颈部肿瘤自适应调强放疗患者基于cT图像自动勾画靶区与危及器官轮廓的吻合度。方法对随机抽取的10例已勾画轮廓的头颈部肿瘤患者设计两种形变自动勾画方式,第1种对每例患者进行基于各自CT图像的形变自动勾画,第2种是随机抽取2例患者CT图像作为参考进行其余8例的形变自动勾画。利用形状相似性系数(DSC)及交叉指数(OI)评估自动勾画与人工勾画的吻合性,并行两种勾画方式差异配对t检验。结果第1种的所有器官DSC和OI值均〉0.80,下颌骨均最高(〉0.91)。大体肿瘤体积DSC值最低(0.81),临床靶体积的为0.82;而临床靶体积OI值最低(0.79),大体肿瘤体积的为0.82。第2种的只对危及器官进行勾画并将脊髓和脑干综合起来分析,所有DSC均在0.70左右,下颌骨DSC和OI值均较高,这与其骨性解剖结构密切相关。第2种勾画结果中绝大多数明显低于第1种的(t=3.24~8.26,P=0.014~0.000),只有右腮腺接近有统计学意义水平(t=2.08,P=0.075)。结论ABAS软件用于自适应调强放疗患者的内部轮廓勾画可达到非常满意结果,而对常规调强放疗患者轮廓勾画应仔细选择参考图像以最大限度满足临床需要。 展开更多
关键词 ABAS自动勾画软件 大体肿瘤体积 临床靶体积 危及器官 头领部肿瘤/ 自适应调强放射疗法
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