Background:The liver is one of the most common sites for the spread of pancreatic ductal adenocarcinoma (PDAC) cells, with metastases present in about 80% of patients. Clinical and preclinical studies of PDAC require quantification of the liver’s metastatic burden from several acquired images, which can benefit from automatic image segmentation tools.Methods:We developed three neural networks based on U-net architecture to automatically segment the healthy liver area (HL), the metastatic liver area (MLA), and liver metastases (LM) in micro-CT images of a mouse model of PDAC with liver metastasis. Three alternative U-nets were trained for each structure to be segmented following appropriate image preprocessing and the one with the highest performance was then chosen and applied for each case.Results:Good performance was achieved, with accuracy of 92.6%, 88.6%, and 91.5%, specificity of 95.5%, 93.8%, and 99.9%, Dice of 71.6%, 74.4%, and 29.9%, and negative predicted value (NPV) of 97.9%, 91.5%, and 91.5% on the pilot validation set for the chosen HL, MLA, and LM networks, respectively.Conclusions:The networks provided good performance and advantages in terms of saving time and ensuring reproducibility.
背景：肝脏是胰腺导管腺癌细胞最常见的转移部位之一，约80%的患者存在肝转移。PDAC的临床与临床前研究需要对多幅获取图像中的肝脏转移负荷进行量化分析，自动图像分割工具可为此提供助力。 方法：我们基于U-net架构开发了三种神经网络，用于在PDAC肝转移小鼠模型的显微CT图像中自动分割健康肝区、转移性肝区及肝转移灶。针对每个待分割结构，在完成适当图像预处理后训练三种替代U-net模型，选取性能最优者分别应用于各案例。 结果：所选健康肝区、转移性肝区及肝转移灶网络在初步验证集上均取得良好性能：准确率分别为92.6%、88.6%和91.5%；特异性分别为95.5%、93.8%和99.9%；Dice系数分别为71.6%、74.4%和29.9%；阴性预测值分别为97.9%、91.5%和91.5%。 结论：该系列神经网络在节约时间与保证可重复性方面展现出良好性能与显著优势。
Automatic Segmentation of Metastatic Livers by Means of U-Net-Based Procedures
肿瘤(癌症)患者之家