Accurate segmentation of lung fields on chest radiographs using deep convolutional networks

Arbabshirani, Mohammad R.; Dallal, Ahmed; Agarwal, Chirag; Patel, Aalpan; Moore, Gregory J.

doi:10.1117/12.2254526

Cited by 23 publications

(13 citation statements)

References 14 publications

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“…Difficulties in distinguishing soft tissue caused by poor contrast in X-Ray images have led some researchers to implement contrast enhancement [22] as a pre-processing step in X-Ray based diagnosis. In addition, lung segmentation of X-Ray images is an important step in the identification of lung nodules and various segmentation approaches are proposed in the literature based on linear filtering/thresholding, rolling ball filters and more recently CNNs [23].…”

Section: Related Workmentioning

confidence: 99%

X-Ray Image based COVID-19 Detection using Pre-trained Deep Learning Models

Horry¹,

Chakraborty²,

Paul³

et al. 2020

Preprint

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Detecting COVID-19 early may help in devising an appropriate treatment plan and disease containment decisions. In this study, we demonstrate how pre-trained deep learning models can be adopted to perform COVID-19 detection using X-Ray images. The aim is to provide over-stressed medical professionals a second pair of eyes through intelligent image classification models. We highlight the challenges (including dataset size and quality) in utilising current publicly available COVID-19 datasets for developing useful deep learning models. We propose a semi-automated image pre-processing model to create a trustworthy image dataset for developing and testing deep learning models. The new approach is aimed to reduce unwanted noise from X-Ray images so that deep learning models can focus on detecting diseases with specific features from them. Next, we devise a deep learning experimental framework, where we utilise the processed dataset to perform comparative testing for several popular and widely available deep learning model families such as VGG, Inception, Xception, and Resnet. The experimental results highlight the suitability of these models for current available dataset and indicates that models with simpler networks such as VGG19 performs relatively better with up to 83% precision. This will provide a solid pathway for researchers and practitioners to develop improved models in the future.

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Section: Related Workmentioning

confidence: 99%

X-Ray Image based COVID-19 Detection using Pre-trained Deep Learning Models

Horry¹,

Chakraborty²,

Paul³

et al. 2020

Preprint

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“…Although our average Error is a litter bit higher than that of Th and FCM because our algorithm caused a little oversegmentation, all of the Errors are lower than 0.008. We randomly select a group of lung CT images with juxta-pleural nodules and ILDs from the dataset, and then segment the images, respectively, with our algorithm and several deep learning (DL) models, 20,43 such as U-Net, block-based CNN (B_CNN), and pixel-based CNN (P_CNN), taking image blocks or pixels as classification units. The average segmentation accuracy is shown in Table III, and it can be seen that our algorithm achieves higher accuracy on lung segmentation.…”

Section: Resultsmentioning

confidence: 99%

A fully automatic segmentation algorithm for CT lung images based on random forest

2019

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Purpose Several negative factors, such as juxta‐pleural nodules, pulmonary vessels, and image noise, make accurately segmenting lungs from computed tomography (CT) images a complex task. We propose a novel hybrid automated algorithm in the paper based on random forest to deal with the issues. Our method aims to eliminate the effect of the factors and generate accurate segmentation of lungs from CT images. Methods Our algorithm consists of five main steps: image preprocessing, lung region extraction, trachea elimination, lung separation, and contour correction. A lung CT image is first preprocessed with a novel normal vector correlation‐based image denoising approach and decomposed into a group of multiscale subimages. A modified superpixel segmentation method is then performed on the first‐level subimage to generate a set of superpixels, and a random forest classifier is employed to segment the lungs by classifying the superpixels of each subimage‐based on the features extracted from them. The initial lung segmentation result is further refined through trachea elimination using an iterative thresholding approach, lung separation based on context information of image sequence, and contour correction with a corner detection technique. Results Our algorithm is tested on a set of CT images affected with interstitial lung diseases, and experiments show that the algorithm achieves high accuracy on lung segmentation with 0.9638 Jaccard’s index and 0.9867 Dice similarity coefficient, compared with ground truths. Additionally, our algorithm achieves an average 7.7% better Dice similarity coefficient than compared conventional lung segmentation methods and 1% better than Deep Learning. Conclusions Our algorithm can segment lungs from lung CT images with good performance in a fully automatic fashion, and it is of great assistance for lung disease detection in the computer‐aided detection system.

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“…However, the usage of deep learning techniques in the area of LFS remains relatively unexplored. LFS methods based on deep learning were presented in [16][17][18].…”

Section: Lung Field Segmentationmentioning

confidence: 99%

Lung segmentation in chest radiographs using fully convolutional networks

Hooda¹,

Mittal²,

Sofat³

2019

Turk J Elec Eng & Comp Sci

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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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Accurate segmentation of lung fields on chest radiographs using deep convolutional networks

Cited by 23 publications

References 14 publications

X-Ray Image based COVID-19 Detection using Pre-trained Deep Learning Models

X-Ray Image based COVID-19 Detection using Pre-trained Deep Learning Models

A fully automatic segmentation algorithm for CT lung images based on random forest

Lung segmentation in chest radiographs using fully convolutional networks

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