不同多模型迭代重建算法与卷积核参数对CT影像自动分割稳定性的影响  

The Effects of Different Adaptive Statistical Iterative Reconstruction-V and Convolution Kernel Parameters on Auto-Segmentation Stability in CT Images

作　　者：姜筱璇 余行 邓中华 朱智慧[1] 傅玉川[1] JIANG Xiaoxuan;YU Hang;DENG Zhonghua;ZHU Zhihui;FU Yuchuan(Department of Radiotherapy,West China Hospital,Sichuan University,Chengdu,610041)

机构地区：[1]四川大学华西医院放疗科,成都市610041

出　　处：《中国医疗器械杂志》2022年第2期219-224,共6页Chinese Journal of Medical Instrumentation

基　　金：四川省科技计划重点研发项目(2020YFS0274)。

摘　　要：目的 探究不同多模型迭代重建算法(ASi R-V)与卷积核重建算法参数对基于深度学习的CT自动分割稳定性的影响。方法 选取20例行盆腔放疗的病人,采用不同的重建参数建立CT影像数据集,利用深度学习神经网络对3个软组织器官(膀胱、肠袋、小肠)和5个骨性器官(左、右股骨头,左、右股骨,骨盆)进行自动分割,并以滤波反投影CT的分割结果为参考,比较不同重建CT上自动分割结果的DSC系数和Hausdorff距离。结果 器官的自动分割受ASi R-V参数影响较大,受卷积核参数影响较小,且在软组织中更加明显。结论 基于深度学习的自动分割稳定性会受到CT图像重建算法参数选择的影响,在实际应用中需在图像质量与分割质量中寻求平衡,或者改进分割网络来提高自动分割的稳定性。Objective The study aims to investigate the effects of different adaptive statistical iterative reconstruction-V(ASi R-V)and convolution kernel parameters on stability of CT auto-segmentation which is based on deep learning.Method Twenty patients who have received pelvic radiotherapy were selected and different reconstruction parameters were used to establish CT images dataset.Then structures including three soft tissue organs(bladder,bowelbag,small intestine) and five bone organs(left and right femoral head,left and right femur,pelvic) were segmented automatically by deep learning neural network.Performance was evaluated by dice similarity coefficient(DSC)and Hausdorff distance,using filter back projection(FBP) as the reference.Results Auto-segmentation of deep learning is greatly affected by ASIR-V,but less affected by convolution kernel,especially in soft tissues.Conclusion The stability of auto-segmentation is affected by parameter selection of reconstruction algorithm.In practical application,it is necessary to find a balance between image quality and segmentation quality,or improve segmentation network to enhance the stability of auto-segmentation.

关 键 词：CT重建算法 多模型迭代重建 卷积核 自动分割 

分 类 号：R815[医药卫生—放射医学]