Versatile approximation of the lung field boundaries in chest radiographs in the presence of bacterial pulmonary infections

Objective To segment lung fields on digital chest radiographs automatically. Methods A morphological reconstruction filter was first applied to the original image to eliminate the local grey level extremes. Then, the Otsu-threshold method was used to segment the whole image into several connected regions, contours of which were then extracted by using a connected components labelling technique. Finally, a morphological closing operator was employed to smooth any small gaps or burrs of the lung field contours. Results Lung fields were segmented for 40 digital chest radiographs, resulting in an accurate segmentation and extraction of their lung fields for most images. Conclusion Integrating a series of algorithms, including morphological reconstruction filtering, Otsu-threshold segmentation, connected components labelling, and morphological operations for smoothing the outlines can effectively segment the lung fields on digital radiographs.

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A deep learning-based model for screening and staging pneumoconiosis

Scientific Reports ◽

10.1038/s41598-020-77924-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Liuzhuo Zhang ◽

Ruichen Rong ◽

Qiwei Li ◽

Donghan M. Yang ◽

Bo Yao ◽

...

Keyword(s):

Deep Learning ◽

Chest Radiographs ◽

Classification Model ◽

Lung Field ◽

Independent Test ◽

Occupational Lung Diseases ◽

Double Blinded ◽

Screening Accuracy ◽

Normal Stage ◽

Better Than

AbstractThis study aims to develop an artificial intelligence (AI)-based model to assist radiologists in pneumoconiosis screening and staging using chest radiographs. The model, based on chest radiographs, was developed using a training cohort and validated using an independent test cohort. Every image in the training and test datasets were labeled by experienced radiologists in a double-blinded fashion. The computational model started by segmenting the lung field into six subregions. Then, convolutional neural network classification model was used to predict the opacity level for each subregion respectively. Finally, the diagnosis for each subject (normal, stage I, II, or III pneumoconiosis) was determined by summarizing the subregion-based prediction results. For the independent test cohort, pneumoconiosis screening accuracy was 0.973, with both sensitivity and specificity greater than 0.97. The accuracy for pneumoconiosis staging was 0.927, better than that achieved by two groups of radiologists (0.87 and 0.84, respectively). This study develops a deep learning-based model for screening and staging of pneumoconiosis using man-annotated chest radiographs. The model outperformed two groups of radiologists in the accuracy of pneumoconiosis staging. This pioneer work demonstrates the feasibility and efficiency of AI-assisted radiography screening and diagnosis in occupational lung diseases.

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View Identification Assisted Fully Convolutional Network for Lung Field Segmentation of Frontal and Lateral Chest Radiographs

IEEE Access ◽

10.1109/access.2021.3074026 ◽

2021 ◽

pp. 1-1

Author(s):

Yuhua Xi ◽

Liming Zhong ◽

Weijie Xie ◽

Genggeng Qin ◽

Yunbi Liu ◽

...

Keyword(s):

Chest Radiographs ◽

Lung Field ◽

Convolutional Network ◽

Fully Convolutional Network ◽

Lateral Chest ◽

Lung Field Segmentation

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SHAPE CHARACTERIZATION OF MEDIASTINUM IN TUBERCULOSIS CHEST RADIOGRAPHS USING LEVEL SET SEGMENTATION

Biomedical Sciences Instrumentation ◽

10.34107/yhpn9422.04212 ◽

2021 ◽

Vol 57 (2) ◽

pp. 212-218

Author(s):

Sukanta Kumar Tulo ◽

◽

Satyavratan Govindarajan ◽

Palaniappan Ramu ◽

Ramakrishnan Swaminathan ◽

...

Keyword(s):

Level Set ◽

Area Ratio ◽

Chest Radiographs ◽

Lung Field ◽

Segmented Images ◽

Segmentation Approach ◽

Edge Based ◽

Level Set Evolution ◽

Shape Characterization

Mediastinum is considered as one of the substantial anatomical regions for the gross diagnosis of several chest related pathologies. The geometric variations of the mediastinum in Chest Radiographs (CXRs) could be utilised as potential image markers in the early detection of Tuberculosis (TB). This study attempts to segment mediastinum in CXRs using level sets for the shape characterization of TB conditions. The CXR images for this study are considered from a public database. An edge-based distance regularized level set evolution is employed to segment the lungs followed by a region-based Chan-Vese model that extracts mediastinum region. Features such as mediastinum area and lungs area are extracted from the segmented images. Further, mediastinum to lungs area ratio is calculated. Statistical analysis is performed on the features to differentiate normal and TB images. Results show that the proposed segmentation approach is able to segment the lungs and extract the mediastinum in CXRs. It is found that features namely mediastinum area and mediastinum to lungs area ratio are statistically significant in the differentiation of TB. Larger mediastinum area is observed in TB images as compared to normal. The performance of lung field segmentation is also observed to be in line with the literature. The mediastinum segmentation approach in CXRs obtains to be a novel method as compared to the existing methods. As the proposed approach based on mediastinum image analysis provides better shape characterization, the study could be clinically useful in the differentiation of TB conditions.

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Lung segmentation in chest radiographs using fully convolutional networks

TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES ◽

10.3906/elk-1710-157 ◽

2019 ◽

pp. 710-722 ◽

Cited By ~ 1

Author(s):

RAHUL HOODA ◽

AJAY MITTAL ◽

SANJEEV SOFAT

Keyword(s):

State Of The Art ◽

Region Of Interest ◽

Chest Radiographs ◽

Lung Field ◽

Convolutional Network ◽

Convolutional Networks ◽

Cad System ◽

Fully Convolutional Networks ◽

Testing Accuracy ◽

Aided Diagnosis

Automated segmentation of medical images that aims at extracting anatomical boundaries is a fundamental step in any computer-aided diagnosis (CAD) system. Chest radiographic CAD systems, which are used to detect pulmonary diseases, first segment the lung field to precisely define the region-of-interest from which radiographic patterns are sought. In this paper, a deep learning-based method for segmenting lung fields from chest radiographs has been proposed. Several modifications in the fully convolutional network, which is used for segmenting natural images to date, have been attempted and evaluated to finally evolve a network fine-tuned for segmenting lung fields. The testing accuracy and overlap of the evolved network are 98.75% and 96.10%, respectively, which exceeds the state-of-the-art results.

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