Deep convolutional neural networks for multiplanar lung nodule detection: Improvement in small nodule identification

Purpose Early detection of lung cancer is of importance since it can increase patients’ chances of survival. To detect nodules accurately during screening, radiologists would commonly take the axial, coronal, and sagittal planes into account, rather than solely the axial plane in clinical evaluation...

Full description

Saved in:
Bibliographic Details
Published in:Medical physics (Lancaster) Vol. 48; no. 2; pp. 733 - 744
Main Authors: Zheng, Sunyi, Cornelissen, Ludo J., Cui, Xiaonan, Jing, Xueping, Veldhuis, Raymond N. J., Oudkerk, Matthijs, Ooijen, Peter M. A.
Format: Journal Article
Language:English
Published: United States John Wiley and Sons Inc 01.02.2021
Subjects:
computed tomography
computer‐aided detection
convolutional neural network
deep learning
Humans
Lung
Lung Neoplasms - diagnostic imaging
Neural Networks, Computer
pulmonary nodule detection
QUANTITATIVE IMAGING AND IMAGE PROCESSING
Radiographic Image Interpretation, Computer-Assisted
Solitary Pulmonary Nodule - diagnostic imaging
Tomography, X-Ray Computed
computed tomography
deep learning
pulmonary nodule detection
computer-aided detection
convolutional neural network
ISSN:0094-2405, 2473-4209, 2473-4209
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Purpose Early detection of lung cancer is of importance since it can increase patients’ chances of survival. To detect nodules accurately during screening, radiologists would commonly take the axial, coronal, and sagittal planes into account, rather than solely the axial plane in clinical evaluation. Inspired by clinical work, the paper aims to develop an accurate deep learning framework for nodule detection by a combination of multiple planes. Methods The nodule detection system is designed in two stages, multiplanar nodule candidate detection, multiscale false positive (FP) reduction. At the first stage, a deeply supervised encoder–decoder network is trained by axial, coronal, and sagittal slices for the candidate detection task. All possible nodule candidates from the three different planes are merged. To further refine results, a three‐dimensional multiscale dense convolutional neural network that extracts multiscale contextual information is applied to remove non‐nodules. In the public LIDC‐IDRI dataset, 888 computed tomography scans with 1186 nodules accepted by at least three of four radiologists are selected to train and evaluate our proposed system via a tenfold cross‐validation scheme. The free‐response receiver operating characteristic curve is used for performance assessment. Results The proposed system achieves a sensitivity of 94.2% with 1.0 FP/scan and a sensitivity of 96.0% with 2.0 FPs/scan. Although it is difficult to detect small nodules (i.e., <6 mm), our designed CAD system reaches a sensitivity of 93.4% (95.0%) of these small nodules at an overall FP rate of 1.0 (2.0) FPs/scan. At the nodule candidate detection stage, results show that the system with a multiplanar method is capable to detect more nodules compared to using a single plane. Conclusion Our approach achieves good performance not only for small nodules but also for large lesions on this dataset. This demonstrates the effectiveness of our developed CAD system for lung nodule detection.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0094-2405
2473-4209
2473-4209
DOI:10.1002/mp.14648