Chen Wen-Fan, Ou Hsin-You, Liu Keng-Hao, Li Zhi-Yun, Liao Chien-Chang, Wang Shao-Yu, Huang Wen, Cheng Yu-Fan, Pan Cheng-Tang
Institute of Medical Science and Technology, National Sun Yat-sen University, Kaohsiung 80424, Taiwan.
Liver Transplantation Program and Departments of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 833401, Taiwan.
Diagnostics (Basel). 2020 Dec 23;11(1):11. doi: 10.3390/diagnostics11010011.
Cancer is one of the common diseases. Quantitative biomarkers extracted from standard-of-care computed tomography (CT) scan can create a robust clinical decision tool for the diagnosis of hepatocellular carcinoma (HCC). According to the current clinical methods, the situation usually accounts for high expenditure of time and resources. To improve the current clinical diagnosis and therapeutic procedure, this paper proposes a deep learning-based approach, called Successive Encoder-Decoder (SED), to assist in the automatic interpretation of liver lesion/tumor segmentation through CT images. The SED framework consists of two different encoder-decoder networks connected in series. The first network aims to remove unwanted voxels and organs and to extract liver locations from CT images. The second network uses the results of the first network to further segment the lesions. For practical purpose, the predicted lesions on individual CTs were extracted and reconstructed on 3D images. The experiments conducted on 4300 CT images and LiTS dataset demonstrate that the liver segmentation and the tumor prediction achieved 0.92 and 0.75 in Dice score, respectively, by as-proposed SED method.
癌症是常见疾病之一。从标准护理计算机断层扫描（CT）图像中提取的定量生物标志物可为肝细胞癌（HCC）的诊断创建一个强大的临床决策工具。按照当前的临床方法，这种情况通常会耗费大量时间和资源。为改进当前的临床诊断和治疗流程，本文提出一种基于深度学习的方法，称为连续编码器 - 解码器（SED），以协助通过CT图像自动解读肝脏病变/肿瘤分割。SED框架由两个串联的不同编码器 - 解码器网络组成。第一个网络旨在去除不需要的体素和器官，并从CT图像中提取肝脏位置。第二个网络利用第一个网络的结果进一步分割病变。出于实际目的，在个体CT上预测的病变被提取并重建到3D图像上。在4300张CT图像和LiTS数据集上进行的实验表明，通过所提出的SED方法，肝脏分割和肿瘤预测的Dice分数分别达到0.92和0.75。
