Huang P, Zhong L, Zheng K, Chen Z, Xiao R, Quan X, Yang W
School of Biomedical Engineering, Southern Medical University//Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou 510515, China.
Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou 510282, China.
Nan Fang Yi Ke Da Xue Xue Bao. 2024 Jan 20;44(1):83-92. doi: 10.12122/j.issn.1673-4254.2024.01.10.
To propose a method for abdominal multi-organ segmentation assisted by multi-phase CT synthesis.
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.
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.
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.
提出一种多期CT合成辅助腹部多器官分割的方法。
采用多期CT合成来生成高质量的CT图像，以增加图像分割的信息细节。引入一个Transformer模块，与感知损失协作以捕捉长距离语义信息，从而最小化真实图像与合成图像之间的差异。
使用南方医院526例的多期CT数据集对模型进行训练。从动脉期CT合成的非增强CT、静脉期增强CT（CECT）和延迟期CECT图像的平均最大绝对误差（MAE）分别为19.192±3.381、20.140±2.676和22.538±2.874，优于使用其他方法合成的图像。多期CT合成辅助腹部多器官分割方法的验证显示，内部验证集的平均骰子系数为0.847，外部验证集为0.823。
所提出的方法能够合成高质量的多期CT图像，有效减少不同期CT图像之间的配准误差，并提高13个腹部器官的分割性能。
