An automatic and efficient pulmonary nodule detection system based on multi-model ensemble

Abstract

Accurate pulmonary nodule detection plays an important role in early screening of lung cancer. Although there are many presented CAD systems based on deep learning for pulmonary nodule detection, these methods still have some problems in clinical use. The improvement of false negatives rate of tiny nodules, the reduction of false alarms and the optimization of time consumption are some of them that need to be solved as soon as possible. In view of the above problems, in this paper, we first propose a novel full convolution segmentation framework for lung cavity extraction in preprocessing stage to solve the time consumption problem of the existing pulmonary nodule detection systems. Furthermore, a 2D-NestedUNet segmentation network and a 3D-RPN detection network is stacked to get the high recall and low false positive rate on nodule candidate extraction, especially the recall of tiny nodules. Finally, a false positive reduction method based on multi-model ensemble is proposed for the further classification of nodule candidates. Our methods are evaluated on several public datasets, LUNA16, LNDb and ChestCT2019, which demonstrated the superior performance of our CAD system. Introduction 1 Lung cancer is a leading cause of cancer-related death both in men and women. Every 2 year lung cancer results in about 170 million deaths worldwide. The early diagnosis of a 3 lung lesion is recognized as the most important method to reduce the lung cancer 4 mortality rate. The low-dose CT scans which is one diagnostic way can be used to 5 screen for lung cancer in people. Using this screening method can decrease the risk of 6 dying from lung cancer. Now researchers are looking for new attempts to refine CT 7 screening to better predict whether cancer is present [1]. Due to the rich pulmonary 8 vascular structure and the different skill levels of radiologists, the potential malignant 9lesions are easy to be ignored. In the clinic, an effective way to deal with this problem is 10 to diagnose by two radiologists respectively and then to summarize their answers.