Functional-realistic CT image super-resolution for early-stage pulmonary nodule detection

2021 ◽  
Vol 115 ◽  
pp. 475-485
Author(s):  
Hongbo Zhu ◽  
Guangjie Han ◽  
Yan Peng ◽  
Wenbo Zhang ◽  
Chuan Lin ◽  
...  
Keyword(s):  
Pulmonary Nodule ◽  
Early Stage ◽  
Super Resolution ◽  
Ct Image ◽  
Nodule Detection ◽  
Pulmonary Nodule Detection ◽  
Image Super Resolution

scholarly journals A Novel Pulmonary Nodule Detection Model Based on Multi-Step Cascaded Networks

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4301 ◽  
Author(s):  
Jianning Chi ◽  
Shuang Zhang ◽  
Xiaosheng Yu ◽  
Chengdong Wu ◽  
Yang Jiang
Keyword(s):  
Pulmonary Nodule ◽  
Receptive Fields ◽  
Lung Parenchyma ◽  
Chest Ct ◽  
Detection Methods ◽  
Computer Assisted ◽  
Ct Image ◽  
Dilated Convolution ◽  
Nodule Detection ◽  
Pulmonary Nodule Detection

Pulmonary nodule detection in chest computed tomography (CT) is of great significance for the early diagnosis of lung cancer. Therefore, it has attracted more and more researchers to propose various computer-assisted pulmonary nodule detection methods. However, these methods still could not provide convincing results because the nodules are easily confused with calcifications, vessels, or other benign lumps. In this paper, we propose a novel deep convolutional neural network (DCNN) framework for detecting pulmonary nodules in the chest CT image. The framework consists of three cascaded networks: First, a U-net network integrating inception structure and dense skip connection is proposed to segment the region of lung parenchyma from the chest CT image. The inception structure is used to replace the first convolution layer for better feature extraction with respect to multiple receptive fields, while the dense skip connection could reuse these features and transfer them through the network. Secondly, a modified U-net network where all the convolution layers are replaced by dilated convolution is proposed to detect the “suspicious nodules” in the image. The dilated convolution can increase the receptive fields to improve the ability of the network in learning global information of the image. Thirdly, a modified U-net adapting multi-scale pooling and multi-resolution convolution connection is proposed to find the true pulmonary nodule in the image with multiple candidate regions. During the detection, the result of the former step is used as the input of the latter step to follow the “coarse-to-fine” detection process. Moreover, the focal loss, perceptual loss and dice loss were used together to replace the cross-entropy loss to solve the problem of imbalance distribution of positive and negative samples. We apply our method on two public datasets to evaluate its ability in pulmonary nodule detection. Experimental results illustrate that the proposed method outperform the state-of-the-art methods with respect to accuracy, sensitivity and specificity.

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