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

Souza, Johnatan Carvalho; Diniz, João Otávio Bandeira; Ferreira, Jonnison Lima; Silva, Giovanni Lucca França da; Silva, Aristófanes Corrêa; Paiva, Anselmo Cardoso de

doi:10.1016/j.cmpb.2019.06.005

Cited by 215 publications

(120 citation statements)

References 27 publications

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“…The SC dataset consists of 662 frontal chest X-Ray images with 326 normal and 336 tuberculosis manifestation cases, where the images were obtained using a Philips DR Digital Diagnostic System. In our experiments with MC, we followed the same criteria as [43]. For the 138 images, 80 images were used for training, 20 images for validation, and 38 images for testing purposes.…”

Section: Lung Segmentation With Other Open Datasets Using X-raynetmentioning

confidence: 99%

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Artificial Intelligence-Based Diagnosis of Cardiac and Related Diseases

Arsalan

Owais

Mahmood

et al. 2020

JCM

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Automatic chest anatomy segmentation plays a key role in computer-aided disease diagnosis, such as for cardiomegaly, pleural effusion, emphysema, and pneumothorax. Among these diseases, cardiomegaly is considered a perilous disease, involving a high risk of sudden cardiac death. It can be diagnosed early by an expert medical practitioner using a chest X-Ray (CXR) analysis. The cardiothoracic ratio (CTR) and transverse cardiac diameter (TCD) are the clinical criteria used to estimate the heart size for diagnosing cardiomegaly. Manual estimation of CTR and other diseases is a time-consuming process and requires significant work by the medical expert. Cardiomegaly and related diseases can be automatically estimated by accurate anatomical semantic segmentation of CXRs using artificial intelligence. Automatic segmentation of the lungs and heart from the CXRs is considered an intensive task owing to inferior quality images and intensity variations using nonideal imaging conditions. Although there are a few deep learning-based techniques for chest anatomy segmentation, most of them only consider single class lung segmentation with deep complex architectures that require a lot of trainable parameters. To address these issues, this study presents two multiclass residual mesh-based CXR segmentation networks, X-RayNet-1 and X-RayNet-2, which are specifically designed to provide fine segmentation performance with a few trainable parameters compared to conventional deep learning schemes. The proposed methods utilize semantic segmentation to support the diagnostic procedure of related diseases. To evaluate X-RayNet-1 and X-RayNet-2, experiments were performed with a publicly available Japanese Society of Radiological Technology (JSRT) dataset for multiclass segmentation of the lungs, heart, and clavicle bones; two other publicly available datasets, Montgomery County (MC) and Shenzhen X-Ray sets (SC), were evaluated for lung segmentation. The experimental results showed that X-RayNet-1 achieved fine performance for all datasets and X-RayNet-2 achieved competitive performance with a 75% parameter reduction.

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Section: Lung Segmentation With Other Open Datasets Using X-raynetmentioning

confidence: 99%

“…Souza et al used a patch-based deep learning approach to lung region segmentation by using an AlexNet-similar structure. The classified pixels are plotted and reconstructed to obtain the fine boundaries [43].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Based Diagnosis of Cardiac and Related Diseases

Arsalan

Owais

Mahmood

et al. 2020

JCM

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“…With the availability of larger datasets in recent years, more researches are now focused on multiple organ segmentation. It has become an active topic in various medical imaging fields [61].…”

Section: B Multiple Organ Segmentationmentioning

confidence: 99%

An Overview of Deep Learning Approaches in Chest Radiograph

et al. 2020

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Chest X-ray (CXR) interpretations are conducted in hospitals and medical facilities on daily basis. If the interpretation tasks were performed correctly, various vital medical conditions of patients can be revealed such as pneumonia, pneumothorax, interstitial lung disease, heart failure and bone fracture. The current practices often involve tedious manual processes dependent on the expertise of radiologist or consultant, thus, the execution is easily prone to human errors of being misdiagnosed. With the recent advances of deep learning and increased hardware computational power, researchers are working on various networks and algorithms to develop machines learning that can assists radiologists in their diagnosis and reduce the probability of misdiagnosis. This paper presents a review of deep learning advancements made in the field of chest radiography. It discusses single and multi-level localization and segmentation techniques adopted by researchers for higher accuracy and precision.

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“…Souza et al [37] used AlexNet-based CNN for lung patch classification. Then, a second CNN model, based on ResNet18, was employed to reconstruct the missing parts of the lung area.…”

Section: Introductionmentioning

confidence: 99%

A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images

et al. 2020

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Pneumonia is among the top diseases which cause most of the deaths all over the world. Virus, bacteria and fungi can all cause pneumonia. However, it is difficult to judge the pneumonia just by looking at chest X-rays. The aim of this study is to simplify the pneumonia detection process for experts as well as for novices. We suggest a novel deep learning framework for the detection of pneumonia using the concept of transfer learning. In this approach, features from images are extracted using different neural network models pretrained on ImageNet, which then are fed into a classifier for prediction. We prepared five different models and analyzed their performance. Thereafter, we proposed an ensemble model that combines outputs from all pretrained models, which outperformed individual models, reaching the state-of-the-art performance in pneumonia recognition. Our ensemble model reached an accuracy of 96.4% with a recall of 99.62% on unseen data from the Guangzhou Women and Children's Medical Center dataset.

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

Cited by 215 publications

References 27 publications

Artificial Intelligence-Based Diagnosis of Cardiac and Related Diseases

Artificial Intelligence-Based Diagnosis of Cardiac and Related Diseases

An Overview of Deep Learning Approaches in Chest Radiograph

A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images

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