首页|基于深度学习的新型妇科后装施源器自动重建系统研发

基于深度学习的新型妇科后装施源器自动重建系统研发

Research and development of a novel automatic reconstruction system based on deep learning for gynecological applicator

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目的:开发一种基于深度学习的施源器自动重建系统,以实现CT引导妇科近距离治疗中Fletcher施源器高效准确地自动重建.方法:施源器自动重建系统分为两个部分:应用2.5D的DpnUNet分割CT图像上的施源器掩膜;通过三维连通区域算法以及骨骼提取算法获取数字化的施源器管道中心线.选取2022年7月至2023年7月在北京协和医院接受妇科近距离放射治疗的68例患者资料,将其中10例患者CT计划作为测试集,将58例患者CT计划采用十折交叉验证方法用于训练,对开发的施源器自动重建系统结果与手动重建结果进行几何一致性比较,并通过三维后装逆向优化计划设计获取剂量学指标高风险临床靶体积(HR-CTV)、90%和98%靶区体积剂量(D90、D98),膀胱、直肠、乙状结肠和小肠的接受最大照射剂量的2 cc体积内的最小剂量(D2cc),探究自动重建系统的临床可用性.结果:在10例测试集患者数据中,自动重建与手动重建的宫腔管以及左右穹窿管中心线顶端平均距离分别为0.335、0.361和0.362 mm,中心线之间的平均豪斯多夫距离(HD)分别为0.398、0.367和0.324 mm;保持驻留位置和驻留时间一致的情况下,两种计划的剂量体积直方图(DVH)参数差异均<2%,具有很高的几何一致性以及临床价值.结论:施源器自动重建系统能够实现高精度的Fletcher施源器的全自动重建,降低潜在的人为错误概率,并提升临床工作效率.
Objective:We have developed a deep learning-based automatic reconstruction system for applicators,aiming to achieve efficient and accurate automatic reconstruction of Fletcher applicators in computed tomography(CT)-guided gynecological brachytherapy.Methods:The automatic reconstruction system of applicator was divided into two main parts.One part was an applicator mask on CT images that was split by 2.5D DpnUNet,and other part was a digitized centerline of the applicator channel that was obtained through three-dimensional(3D)connected region algorithm and skeleton extraction algorithm.The documents of 68 patients who received gynecological brachytherapy in Peking Union Medical College Hospital from July of 2022 to July of 2023 were selected.The CT plans of 10 patients of them were used as test set,and the CT plans of other 58 patients were used to train by adopting 10-fold cross validation method.The comparison of geometric consistency was conducted between the results of developed automatic reconstruction system and the results of manual reconstruction.The high risk clinical target volume(HR-CTV),90%and 98%dose target volume(D90,D98)of dosimetric indicators,as well as the minimum dose(D2cc)within 2cc volume that received maximum exposure dose on bladder,rectum,sigmoid colon and small intestine,were designed and obtained through the 3D rear mounted reverse optimization plan.And then,the clinical feasibility of the automatic reconstruction system was further explored.Results:In the data of 10 patients of test set,the average distances of automatic reconstruction,manual reconstruction and the top-end of the centerline of left-right fornix canal were respectively 0.335,0.361 and 0.362 mm.The average Hausdorff distances between the centerlines were respectively 0.398,0.367 and 0.324 mm.Additionally,the differences of dose-volume histogram(DVH)parameters between the two types of plans was less than 2%under kept the consistency between location and duration of stay.There were very high geometric consistency and clinical value between the two types of plans.Conclusion:The automatic reconstruction system of applicators can realize fully-automatic reconstruction with high-precision of Fletcher applicators,and reduce the probability of potential human error and improve clinical work efficiency.

Deep learningBrachytherapyGynecological applicator

张文君、于浪、张杰、杨波、罗春丽、邱杰

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中国医学科学院 北京协和医学院 北京协和医院放疗科 北京 100730

深度学习 近距离治疗 妇科施源器

中央高水平医院临床科研项目北京协和医院中央高水平医院临床科研专项青年培优计划(2022)

2022-PUMCH-B-0522022-PUMCH-A-101

2024

中国医学装备
中国医学装备协会

中国医学装备

CSTPCD
影响因子:0.882
ISSN:1672-8270
年,卷(期):2024.21(4)
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