摘要
目的 提出并探讨使用多期相CT合成辅助腹部多器官分割方法.方法 提出多期相CT合成辅助腹部多器官分割,多期相CT能够充分提供同一器官不同的图像细节,从而为分割模型提供充分的全面的语义信息,提升腹部多个器官分割的性能.提出基于多头自注意力感知的多期相CT合成方法,引入基于多头自注意力机制的Transformer模块,提升合成网络捕捉长距离语义信息的能力,扩大网络的感受野,并且引入感知损失,在特征层面对合成图像与真实图像特征之间的差异最小化,与Transformer模块有协同作用,从而合成出更清晰、更高质量的多期相CT图像.结果 使用南方医院的多期相CT数据集训练模型.其中用526例多期相CT训练合成模型,利用动脉期增强动脉CT(A.CECT)合成出平扫CT(NECT)、静脉期CECT(V.CECT)、延迟期CECT(D.CECT)的平均最大化绝对误差(MAE)分别为19.192±3.381、20.140±2.676、22.538±2.874,结合统计学对比,本文方法优于对比的其他图像合成方法(P<0.05).多期相CT合成辅助的腹部多器官分割方法验证在内部验证集上进行验证平均Dice系数(DSC)为0.847,在外部验证集上进行验证平均DSC为0.823.结论 本文方法能够合成出高质量的多期相CT图像以有效缓解不同期相CT之间存在的配准无法解决的误差问题,同时提高腹部13器官的分割性能,具有良好的泛化性能.
Abstract
Objective To propose a method for abdominal multi-organ segmentation assisted by multi-phase CT synthesis.Methods Multi-phase CT synthesis for synthesizing high-quality CT images was used to increase the information details for image segmentation.A transformer block was introduced to help to capture long-range semantic information in cooperation with perceptual loss to minimize the differences between the real image and synthesized image.Results The model was trained using multi-phase CT dataset of 526 total cases from Nanfang Hospital.The mean maximum absolute error(MAE)of the synthesized non-contrast CT,venous phase contrast-enhanced CT(CECT),and delay phase CECT images from arterial phase CECT was 19.192±3.381,20.140±2.676 and 22.538±2.874,respectively,which were better than those of images synthesized using other methods.Validation of the multi-phase CT synthesis-assisted abdominal multi-organ segmentation method showed an average dice coefficient of 0.847 for the internal validation set and 0.823 for the external validation set.Conclusion The propose method is capable of synthesizing high-quality multi-phase CT images to effectively reduce the errors in registration between different phase CT images and improve the performance for segmentation of 13 abdominal organs.
国家科技期刊平台
NSTL
维普
万方数据