基于条件生成对抗网络的三维肝脏及肿瘤区域自动分割

肝脏计算机断层扫描成像(CT)的三维(3D)肝脏和肿瘤分割对于辅助医生的诊断及预后具有非常重要的临床价值。为了准确快速地分割肝脏及肿瘤区域,本文提出了一种基于条件生成对抗网络(cGAN)的肿瘤3D条件生成对抗分割网络(T3scGAN),同时采用了一个由粗到细的3D自动分割框架对肝脏及肿瘤区域实施精准分割。本文采用2017年肝脏和肿瘤分割挑战赛(LiTS)公开数据集中的130个病例进行训练、验证和测试T3scGAN模型。最终3D肝脏区域分割的验证集和测试集的平均戴斯(Dice)系数分别为0.963和0.961,而3D肿瘤区域分割的验证集和测试集的平均Dice系数分别为0.819和0.796。实验结果表明,提出的T3scGAN模型能够有效地分割3D肝脏及其肿瘤区域,因此能够更好地辅助医生进行肝脏肿瘤的精准诊断和治疗。

Automatic three-dimensional segmentation of liver and tumors regions based on conditional generative adversarial networks

The three-dimensional(3D)liver and tumor segmentation of liver computed tomography(CT)has very important clinical value for assisting doctors in diagnosis and prognosis.This paper proposes a tumor 3D conditional generation confrontation segmentation network(T3 scGAN)based on conditional generation confrontation network(cGAN),and at the same time,a coarse-to-fine 3 D automatic segmentation framework is used to accurately segment liver and tumor area.This paper uses 130 cases in the 2017 Liver and Tumor Segmentation Challenge(LiTS)public data set to train,verify and test the T3 scGAN model.Finally,the average Dice coefficients of the validation set and test set segmented in the 3 D liver regions were 0.963 and 0.961,respectively,while the average Dice coefficients of the validation set and test set segmented in the 3 D tumor regions were 0.819 and 0.796,respectively.Experimental results show that the proposed T3 scGAN model can effectively segment the 3 D liver and its tumor regions,so it can better assist doctors in the accurate diagnosis and treatment of liver cancer.