scholarly journals Pulmonary Nodule Detection in Chest Ct Using a Deep Learning-Based Reconstruction Algorithm

2021 ◽  
Author(s):  
C Franck ◽  
A Snoeckx ◽  
M Spinhoven ◽  
H El Addouli ◽  
S Nicolay ◽  
...  
Keyword(s):  
Deep Learning ◽  
Pulmonary Nodule ◽  
Reconstruction Algorithm ◽  
Chest Ct ◽  
Reconstruction Algorithms ◽  
Point Scale ◽  
Chest Computed Tomography ◽  
Significant Difference ◽  
Nodule Detection ◽  
Pulmonary Nodule Detection

Abstract This study’s aim was to assess whether deep learning image reconstruction (DLIR) techniques are non-inferior to ASIR-V for the clinical task of pulmonary nodule detection in chest computed tomography. Up to 6 (range 3–6, mean 4.2) artificial lung nodules (diameter: 3, 5, 8 mm; density: −800, −630, +100 HU) were inserted at different locations in the Kyoto Kagaku Lungman phantom. In total, 16 configurations (10 abnormal, 6 normal) were scanned at 7.6, 3, 1.6 and 0.38 mGy CTDIvol (respectively 0, 60, 80 and 95% dose reduction). Images were reconstructed using 50% ASIR-V and a deep learning-based algorithm with low (DL-L), medium (DL-M) and high (DL-H) strength. Four chest radiologists evaluated 256 series by locating and scoring nodules on a five-point scale. No statistically significant difference was found among the reconstruction algorithms (p = 0.987, average across readers AUC: 0.555, 0.561, 0.557, 0.558 for ASIR-V, DL-L, DL-M, DL-H).

Pulmonary Nodule Detection using Chest Ct Images

2003 ◽  
Vol 44 (3) ◽  
pp. 252-257 ◽  
Author(s):  
D.-Y. Kim ◽  
J.-H. Kim ◽  
S.-M. Noh ◽  
J.-W. Park
Keyword(s):  
Pulmonary Nodule ◽  
Ct Images ◽  
Chest Ct ◽  
Nodule Detection ◽  
Pulmonary Nodule Detection

scholarly journals The effect of the reconstruction algorithm for the pulmonary nodule detection under the metal artifact caused by a pacemaker

Medicine ◽  
2020 ◽  
Vol 99 (24) ◽  
pp. e20579
Author(s):  
Noriko Kikuchi ◽  
Masahiro Yanagawa ◽  
Yukihiro Enchi ◽  
Akiko Nakayama ◽  
Yuriko Yoshida ◽  
...  
Keyword(s):  
Pulmonary Nodule ◽  
Reconstruction Algorithm ◽  
Metal Artifact ◽  
Nodule Detection ◽  
Pulmonary Nodule Detection

scholarly journals The Performance of Deep Learning Algorithms on Automatic Pulmonary Nodule Detection and Classification Tested on Different Datasets That Are Not Derived from LIDC-IDRI: A Systematic Review

Diagnostics ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 207 ◽  
Author(s):  
Dana Li ◽  
Bolette Mikela Vilmun ◽  
Jonathan Frederik Carlsen ◽  
Elisabeth Albrecht-Beste ◽  
Carsten Ammitzbøl Lauridsen ◽  
...  
Keyword(s):  
Neural Network ◽  
Systematic Review ◽  
Deep Learning ◽  
Pulmonary Nodule ◽  
Image Database ◽  
Ct Scans ◽  
Learning Technology ◽  
Nodule Detection ◽  
Pulmonary Nodule Detection ◽  
And Training

The aim of this study was to systematically review the performance of deep learning technology in detecting and classifying pulmonary nodules on computed tomography (CT) scans that were not from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) database. Furthermore, we explored the difference in performance when the deep learning technology was applied to test datasets different from the training datasets. Only peer-reviewed, original research articles utilizing deep learning technology were included in this study, and only results from testing on datasets other than the LIDC-IDRI were included. We searched a total of six databases: EMBASE, PubMed, Cochrane Library, the Institute of Electrical and Electronics Engineers, Inc. (IEEE), Scopus, and Web of Science. This resulted in 1782 studies after duplicates were removed, and a total of 26 studies were included in this systematic review. Three studies explored the performance of pulmonary nodule detection only, 16 studies explored the performance of pulmonary nodule classification only, and 7 studies had reports of both pulmonary nodule detection and classification. Three different deep learning architectures were mentioned amongst the included studies: convolutional neural network (CNN), massive training artificial neural network (MTANN), and deep stacked denoising autoencoder extreme learning machine (SDAE-ELM). The studies reached a classification accuracy between 68–99.6% and a detection accuracy between 80.6–94%. Performance of deep learning technology in studies using different test and training datasets was comparable to studies using same type of test and training datasets. In conclusion, deep learning was able to achieve high levels of accuracy, sensitivity, and/or specificity in detecting and/or classifying nodules when applied to pulmonary CT scans not from the LIDC-IDRI database.

Pediatric chest CT radiation dose reduction: protocol refinement based on noise injection for pulmonary nodule detection accuracy

2013 ◽  
Vol 37 (2) ◽  
pp. 334-341 ◽  
Author(s):  
Teresa Chapman ◽  
Jonathan O. Swanson ◽  
Grace S. Phillips ◽  
Marguerite T. Parisi ◽  
Adam M. Alessio
Keyword(s):  
Radiation Dose ◽  
Dose Reduction ◽  
Pulmonary Nodule ◽  
Chest Ct ◽  
Detection Accuracy ◽  
Nodule Detection ◽  
Noise Injection ◽  
Pediatric Chest ◽  
Ct Radiation ◽  
Pulmonary Nodule Detection

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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