Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural Networks

Abstract

Early detection of pulmonary cancer is the most promising way to enhance a patient's chance for survival. Accurate pulmonary nodule detection in computed tomography (CT) images is a crucial step in diagnosing pulmonary cancer. In this paper, inspired by the successful use of deep convolutional neural networks (DCNNs) in natural image recognition, we propose a novel pulmonary nodule detection approach based on DCNNs. We first introduce a deconvolutional structure to Faster Region-based Convolutional Neural Network (Faster R-CNN) for candidate detection on axial slices. Then, a three-dimensional DCNN is presented for the subsequent false positive reduction. Experimental results of the LUng Nodule Analysis 2016 (LUNA16) Challenge demonstrate the superior detection performance of the proposed approach on nodule detection(average FROC-score of 0.891, ranking the 1st place over all submitted results).

MICCAI 2017 accepted

References in corpus (3)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Wide Residual Networks
• Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge

Cited by in corpus (18)

• Recent advances and clinical applications of deep learning in medical image analysis
• SCPM-Net: An Anchor-free 3D Lung Nodule Detection Network using Sphere Representation and Center Points Matching
• 3D RoI-aware U-Net for Accurate and Efficient Colorectal Tumor Segmentation
• DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification
• Propagating Uncertainty in Multi-Stage Bayesian Convolutional Neural Networks with Application to Pulmonary Nodule Detection
• RUN:Residual U-Net for Computer-Aided Detection of Pulmonary Nodules without Candidate Selection
• Joint Learning for Pulmonary Nodule Segmentation, Attributes and Malignancy Prediction
• DeepEM: Deep 3D ConvNets With EM For Weakly Supervised Pulmonary Nodule Detection
• Towards Single-phase Single-stage Detection of Pulmonary Nodules in Chest CT Imaging
• DeepLung: 3D Deep Convolutional Nets for Automated Pulmonary Nodule Detection and Classification
• S4ND: Single-Shot Single-Scale Lung Nodule Detection
• Deep Learning for Automated Medical Image Analysis
• Deep multiscale convolutional feature learning for weakly supervised localization of chest pathologies in X-ray images
• AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging: Towards Radiophenomics
• An End-to-end Framework For Integrated Pulmonary Nodule Detection and False Positive Reduction
• Multi-Scale Gradual Integration CNN for False Positive Reduction in Pulmonary Nodule Detection
• Automated pulmonary nodule detection using 3D deep convolutional neural networks
• Integrating Feature and Image Pyramid: A Lung Nodule Detector Learned in Curriculum Fashion