Diagnosis of Lung segmentation for Chest X Ray images using XGBoost

2021 ◽  
Author(s):  
Preeti Arora ◽  
Saksham Gera ◽  
Vinod M Kapse
Keyword(s):  
Lung Segmentation ◽  
X Ray ◽  
Chest X Ray

scholarly journals Impact of Lung Segmentation on the Diagnosis and Explanation of COVID-19 in Chest X-ray Images

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7116
Author(s):  
Lucas O. Teixeira ◽  
Rodolfo M. Pereira ◽  
Diego Bertolini ◽  
Luiz S. Oliveira ◽  
Loris Nanni ◽  
...  
Keyword(s):  
Semantic Segmentation ◽  
Image Database ◽  
Dice Coefficient ◽  
Lung Segmentation ◽  
X Ray ◽  
Explainable Artificial Intelligence ◽  
Segmented Images ◽  
Chest X Ray ◽  
The Impact ◽  
Different Sources

COVID-19 frequently provokes pneumonia, which can be diagnosed using imaging exams. Chest X-ray (CXR) is often useful because it is cheap, fast, widespread, and uses less radiation. Here, we demonstrate the impact of lung segmentation in COVID-19 identification using CXR images and evaluate which contents of the image influenced the most. Semantic segmentation was performed using a U-Net CNN architecture, and the classification using three CNN architectures (VGG, ResNet, and Inception). Explainable Artificial Intelligence techniques were employed to estimate the impact of segmentation. A three-classes database was composed: lung opacity (pneumonia), COVID-19, and normal. We assessed the impact of creating a CXR image database from different sources, and the COVID-19 generalization from one source to another. The segmentation achieved a Jaccard distance of 0.034 and a Dice coefficient of 0.982. The classification using segmented images achieved an F1-Score of 0.88 for the multi-class setup, and 0.83 for COVID-19 identification. In the cross-dataset scenario, we obtained an F1-Score of 0.74 and an area under the ROC curve of 0.9 for COVID-19 identification using segmented images. Experiments support the conclusion that even after segmentation, there is a strong bias introduced by underlying factors from different sources.

An automatic method for lung segmentation and reconstruction in chest X-ray using deep neural networks

2019 ◽  
Vol 177 ◽  
pp. 285-296 ◽  
Author(s):  
Johnatan Carvalho Souza ◽  
João Otávio Bandeira Diniz ◽  
Jonnison Lima Ferreira ◽  
Giovanni Lucca França da Silva ◽  
Aristófanes Corrêa Silva ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Automatic Method ◽  
Lung Segmentation ◽  
X Ray ◽  
Chest X Ray

scholarly journals Two-stream collaborative network for multi-label chest X-ray Image classification with lung segmentation

2020 ◽  
Vol 135 ◽  
pp. 221-227 ◽  
Author(s):  
Bingzhi Chen ◽  
Zheng Zhang ◽  
Jianyong Lin ◽  
Yi Chen ◽  
Guangming Lu
Keyword(s):  
Image Classification ◽  
Collaborative Network ◽  
Lung Segmentation ◽  
X Ray ◽  
Chest X Ray

scholarly journals A Robust Automated Lung Segmentation System for Chest X-ray (CXR) Images

2017 ◽  
Vol V6 (04) ◽  
Author(s):  
Akhila K J ◽  
Ajitha M ◽  
Ashy V Daniel ◽  
Ashwin Singerji ◽  
Keyword(s):  
Lung Segmentation ◽  
X Ray ◽  
Chest X Ray

scholarly journals Lung Segmentation Considering Global and Local Properties in Chest X-ray Images

2013 ◽  
Vol 16 (7) ◽  
pp. 829-840 ◽  
Author(s):  
Woong-Gi Jeon ◽  
Tae-Yun Kim ◽  
Sung Jun Kim ◽  
Heung-Kuk Choi ◽  
Kwang Gi Kim
Keyword(s):  
Lung Segmentation ◽  
X Ray ◽  
Chest X Ray ◽  
Local Properties ◽  
Global And Local

scholarly journals Development of a lung segmentation algorithm for analog imaged chest X-Ray: preliminary results

2021 ◽  
Author(s):  
Matheus A. Renzo ◽  
Natália Fernandez ◽  
André A. Baceti ◽  
Natanael Nunes Moura Junior ◽  
André Anjos
Keyword(s):  
Semantic Segmentation ◽  
X Rays ◽  
Computer Aided Detection ◽  
Segmentation Evaluation ◽  
Lung Segmentation ◽  
X Ray ◽  
Chest X Ray ◽  
Digitized Images ◽  
Public Datasets ◽  
The Impact

Analog X-Ray radiography is still used in many underdeveloped regions around the world. To allow these populations to benefit from advances in automatic computer-aided detection (CAD) systems, X-Ray films must be digitized. Unfortunately, this procedure may introduce artefacts which may severely impair the performance of such systems. This work investigates the impact digitized images may cause to deep neural networks trained for lung (semantic) segmentation on digital x-ray samples. While three public datasets for lung segmentation evaluation exist for digital samples, none are available for digitized data. To this end, a U-Net architecture was trained on publicly available data, and used to predict lung segmentation on a newly annotated set of digitized images. Our results show that the model is capable to effectively identify lung segmentation at digital X-Rays with a high intra-dataset (PR AUC: 0.99) and cross-dataset (PR AUC: 0.99) performances on unseen test data. When challenged against analog imaged films, the performance is substantially degraded (PR AUC: 0.90). Our analysis further suggests that the use of maximum F1 and precision-recall AUC (PR AUC) measures are not informative to identify segmentation problems in images.

scholarly journals Kerley A-lines represent thickened septal plates between lung segments in patients with lymphangitic carcinomatosis: confirmation using 3D-CT lung segmentation analysis

2021 ◽  
Author(s):  
Nanae Tsuchiya ◽  
Maho Tsubakimoto ◽  
Akihiro Nishie ◽  
Sadayuki Murayama
Keyword(s):  
Pulmonary Edema ◽  
3D Ct ◽  
Anatomic Structure ◽  
Bronchial Wall ◽  
Lung Segmentation ◽  
X Ray ◽  
Segmentation Analysis ◽  
Chest X Ray ◽  
Lymphangitic Carcinomatosis ◽  
Maximum Interval

Abstract Purpose Kerley A-lines are generally apparent in patients with pulmonary edema or lymphangitic carcinomatosis. There are two main thoughts regarding the etiology of Kerley A-lines, but no general agreement. Specifically, the lines are caused by thickened interlobular septa or dilated anastomotic lymphatics. Our purpose was to determine the anatomic structure represented as Kerley A-lines using 3D-CT lung segmentation analysis. Materials and methods We reviewed 139 charts of patients with lymphangitic carcinomatosis of the lung who had CT and X-ray exams with a maximum interval of 7 days. The presence of Kerley A-lines on X-ray was assessed by a radiologist. The A-lines on X-ray were defined as follows: dense; fine (< 1 mm thick); ≥ 2 cm in length, radiating from the hilum; no bifurcation; and not adjacent to the pleura. For cases with Kerley A-lines on X-ray, three radiologists agreed that the lines on CT corresponded with Kerley A-lines. The incidence of A-lines and the characteristics of the lines were investigated. The septal lines between lung segments were identified using a 3D-CT lung segmentation analysis workstation. The percentage of agreement between the A-lines on CT and lung segmental lines was assessed. Results On chest X-ray, 37 Kerley A-lines (right, 16; left, 21) were identified in the 22 cases (16%). Of these, 4 lungs with 12 lines were excluded from analysis due to technical reasons. Nineteen of the 25 lines (76%) corresponded to the septal lines on CT. Of these, 11 lines matched with automatically segmented lines (intersegmental septa, 4; intersubsegmental septa, 7) by the workstation. Two lines (8%) represented fissures. Four lines corresponded to the bronchial wall/artery (3 lines, 12%) or vein (1 line, 4%). Conclusion Kerley A-lines primarily represented thickened and continued interlobular septal lines that corresponded to the septa between lung segments and subsegments.

scholarly journals Automatic Lung Segmentation Algorithm on Chest X-ray Images Based on Fusion Variational Auto-Encoder and Three-Terminal Attention Mechanism

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 814
Author(s):  
Feidao Cao ◽  
Huaici Zhao
Keyword(s):  
Automatic Segmentation ◽  
Attention Mechanism ◽  
Segmentation Algorithm ◽  
Target Area ◽  
Lung Segmentation ◽  
X Ray ◽  
Chest X Ray ◽  
Left And Right ◽  
High Level ◽  
Better Than

Automatic segmentation of the lungs in Chest X-ray images (CXRs) is a key step in the screening and diagnosis of related diseases. There are many opacities in the lungs in the CXRs of patients, which makes the lungs difficult to segment. In order to solve this problem, this paper proposes a segmentation algorithm based on U-Net. This article introduces variational auto-encoder (VAE) in each layer of the decoder-encoder. VAE can extract high-level semantic information, such as the symmetrical relationship between the left and right thoraxes in most cases. The fusion of the features of VAE and the features of convolution can improve the ability of the network to extract features. This paper proposes a three-terminal attention mechanism. The attention mechanism uses the channel and spatial attention module to automatically highlight the target area and improve the performance of lung segmentation. At the same time, the three-terminal attention mechanism uses the advanced semantics of high-scale features to improve the positioning and recognition capabilities of the attention mechanism, suppress background noise, and highlight target features. Experimental results on two different datasets show that the accuracy (ACC), recall (R), F1-Score and Jaccard values of the algorithm proposed in this paper are the highest on the two datasets, indicating that the algorithm in this paper is better than other state-of-the-art algorithms.

Sign in / Sign up

Export Citation Format

Share Document