论文标题
在计算机断层扫描中使用卷积神经网络对对抗进行对抗的肺断层扫描中的肺部分割和结节检测
Lung Segmentation and Nodule Detection in Computed Tomography Scan using a Convolutional Neural Network Trained Adversarially using Turing Test Loss
论文作者
论文摘要
肺癌是世界范围内发现的最常见的癌症形式，死亡率很高。通过使用低剂量计算机断层扫描（CT）扫描筛查对肺结节的早期检测对于其有效的临床管理至关重要。发生恶性肿瘤的结节在患者的CT扫描中占据了约0.0125-025 \％。所有切片的手动筛选是一项繁琐的任务，并提出了人为错误的高风险。为了解决这个问题，我们提出了一个计算有效的两个阶段框架。在第一阶段，卷积神经网络（CNN）使用图灵测试损失部分在肺部区域进行了对抗。在第二阶段，然后对从分段区域采样的斑块进行分类以检测结节的存在。该方法在LUNA16挑战数据集上进行了实验验证，其骰子系数为$ 0.984 \ pm0.0007 $，用于10倍交叉验证。
Lung cancer is the most common form of cancer found worldwide with a high mortality rate. Early detection of pulmonary nodules by screening with a low-dose computed tomography (CT) scan is crucial for its effective clinical management. Nodules which are symptomatic of malignancy occupy about 0.0125 - 0.025\% of volume in a CT scan of a patient. Manual screening of all slices is a tedious task and presents a high risk of human errors. To tackle this problem we propose a computationally efficient two stage framework. In the first stage, a convolutional neural network (CNN) trained adversarially using Turing test loss segments the lung region. In the second stage, patches sampled from the segmented region are then classified to detect the presence of nodules. The proposed method is experimentally validated on the LUNA16 challenge dataset with a dice coefficient of $0.984\pm0.0007$ for 10-fold cross-validation.
