Fully Convolutional Multi-Scale ScSE-DenseNet for Automatic Pneumothorax Segmentation in Chest Radiographs

Luo, Guangchun; Liu, Zhiqin; Wang, Qingfeng; Liu, Qiyu; Zhou, Ying; Xu, Wencai; Huang, Jun; Fu, Jie; Cheng, Jie-Zhi

doi:10.1109/bibm47256.2019.8983004

Cited by 8 publications

(7 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Luo et al (2019), the researchers proposed the fully convolutional DenseNet (FC-DenseNet) with a spatial and channel squeeze, excitation module (scSE), and a multi-scale module for pneumothorax segmentation. They evaluated their approach on 11,051 chest X-rays and achieved 0.93 of Mean Pixel-wise Accuracy (MPA) and 0.92 of DSC.…”

Section: Resultsmentioning

confidence: 99%

“…They evaluated their model on the Chest X-ray dataset ( Wang et al, 2017b ) containing more than 100,000 chest radiography and achieved a magnitude of 0.911 of Area Under the ROC Curve (AUC). In Luo et al (2019) , the researchers proposed the fully convolutional DenseNet (FC-DenseNet) with a spatial and channel squeeze, excitation module (scSE), and a multi-scale module for pneumothorax segmentation. They evaluated their approach on 11,051 chest X-rays and achieved 0.93 of Mean Pixel-wise Accuracy (MPA) and 0.92 of DSC.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Chest X-ray pneumothorax segmentation using U-Net with EfficientNet and ResNet architectures

Abedalla

Abdullah

Al‐Ayyoub

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

Medical imaging refers to visualization techniques to provide valuable information about the internal structures of the human body for clinical applications, diagnosis, treatment, and scientific research. Segmentation is one of the primary methods for analyzing and processing medical images, which helps doctors diagnose accurately by providing detailed information on the body’s required part. However, segmenting medical images faces several challenges, such as requiring trained medical experts and being time-consuming and error-prone. Thus, it appears necessary for an automatic medical image segmentation system. Deep learning algorithms have recently shown outstanding performance for segmentation tasks, especially semantic segmentation networks that provide pixel-level image understanding. By introducing the first fully convolutional network (FCN) for semantic image segmentation, several segmentation networks have been proposed on its basis. One of the state-of-the-art convolutional networks in the medical image field is U-Net. This paper presents a novel end-to-end semantic segmentation model, named Ens4B-UNet, for medical images that ensembles four U-Net architectures with pre-trained backbone networks. Ens4B-UNet utilizes U-Net’s success with several significant improvements by adapting powerful and robust convolutional neural networks (CNNs) as backbones for U-Nets encoders and using the nearest-neighbor up-sampling in the decoders. Ens4B-UNet is designed based on the weighted average ensemble of four encoder-decoder segmentation models. The backbone networks of all ensembled models are pre-trained on the ImageNet dataset to exploit the benefit of transfer learning. For improving our models, we apply several techniques for training and predicting, including stochastic weight averaging (SWA), data augmentation, test-time augmentation (TTA), and different types of optimal thresholds. We evaluate and test our models on the 2019 Pneumothorax Challenge dataset, which contains 12,047 training images with 12,954 masks and 3,205 test images. Our proposed segmentation network achieves a 0.8608 mean Dice similarity coefficient (DSC) on the test set, which is among the top one-percent systems in the Kaggle competition.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Chest X-ray pneumothorax segmentation using U-Net with EfficientNet and ResNet architectures

Abedalla

Abdullah

Al‐Ayyoub

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…The proposed method can not only reduce the impact of class imbalance, but also better describe the boundary areas to segment and diagnose pneumothorax accurately. This study extends our preliminary work [23] by redesigning the automatic segmentation and diagnosis framework for PTX, adding extensive experiments to evaluate the automatic segmentation and diagnosis of PTX, and discussing the effects of different growth rates and loss functions on PTX segmentation.…”

Section: Introductionmentioning

confidence: 82%

Automated segmentation and diagnosis of pneumothorax on chest X-rays with fully convolutional multi-scale ScSE-DenseNet: a retrospective study

Wang

Liu

Luo

et al. 2020

BMC Med Inform Decis Mak

Self Cite

View full text Add to dashboard Cite

Background Pneumothorax (PTX) may cause a life-threatening medical emergency with cardio-respiratory collapse that requires immediate intervention and rapid treatment. The screening and diagnosis of pneumothorax usually rely on chest radiographs. However, the pneumothoraces in chest X-rays may be very subtle with highly variable in shape and overlapped with the ribs or clavicles, which are often difficult to identify. Our objective was to create a large chest X-ray dataset for pneumothorax with pixel-level annotation and to train an automatic segmentation and diagnosis framework to assist radiologists to identify pneumothorax accurately and timely. Methods In this study, an end-to-end deep learning framework is proposed for the segmentation and diagnosis of pneumothorax on chest X-rays, which incorporates a fully convolutional DenseNet (FC-DenseNet) with multi-scale module and spatial and channel squeezes and excitation (scSE) modules. To further improve the precision of boundary segmentation, we propose a spatial weighted cross-entropy loss function to penalize the target, background and contour pixels with different weights. Results This retrospective study are conducted on a total of eligible 11,051 front-view chest X-ray images (5566 cases of PTX and 5485 cases of Non-PTX). The experimental results show that the proposed algorithm outperforms the five state-of-the-art segmentation algorithms in terms of mean pixel-wise accuracy (MPA) with $$0.93\pm 0.13$$ 0.93 ± 0.13 and dice similarity coefficient (DSC) with $$0.92\pm 0.14$$ 0.92 ± 0.14 , and achieves competitive performance on diagnostic accuracy with 93.45% and $$F_1$$ F 1 -score with 92.97%. Conclusion This framework provides substantial improvements for the automatic segmentation and diagnosis of pneumothorax and is expected to become a clinical application tool to help radiologists to identify pneumothorax on chest X-rays.

show abstract

“…A fixed resolution of input image is required in any CAD system. Thus, resizing of the CXR image is a very common step in most of the proposed models for automatic diagnosis of chest pathologies including pneumothorax [41]- [45]. Some of the common techniques for resizing of the CXR images include downsampling, upsampling by nearest-neighborinterpolation and bilinear interpolation [46], [47].…”

Section: A Formatting and Resizingmentioning

confidence: 99%

“…Another cost-sensitive technique is to manipulate the learning rate so that samples with more cost have greater contribution in updating the weights and then network training is carried out with the aim to lessen the misclassification cost [91]. Utilization of loss function such as Cross-Entropy loss function (CEL), and weighted CEL (W-CEL) are relatively new approaches to solve the class imbalance problem [27], [41], [75], [92].…”

Section: ) Algortihm-level Approachesmentioning

confidence: 99%

Automatic Diagnosis of Pneumothorax From Chest Radiographs: A Systematic Literature Review

Iqbal

Shaukat²,

Akram

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Among various medical imaging tools, chest radiographs are the most important and widely used diagnostic tool for the detection of thoracic pathologies. Remarkable research is being carried out to propose robust automatic diagnostic tools for the detection of pathologies from the chest radiographs. Artificial Intelligence techniques have been found to give promising results in automating the field of medicine. Lot of research has been done for automatic detection of pneumothorax from chest radiographs while proposing several frameworks based on artificial intelligence techniques. Undoubtedly, several models are available for automatic diagnosis of pneumothorax, however a summarized review of the existing literature is still missing. This study summarizes the existing literature for pneumothorax detection from chest x-rays along with describing the available chest radiographs datasets. It will help the researchers to select the optimal and most effective model with respect to the real-time scenarios. The comparative analysis of the literature is provided in terms of goodness, usability and quality along with highlighting the research gaps for further investigation. From the literature, it is evident that pneumothorax is more common in men as compared to women. Additionally, the proposed models have achieved incredible results for pneumothorax detection on selected datasets, however, the effectiveness of proposed models in real-time cases cannot be claimed, as none of the models have been implemented clinically yet. This issue can be solved by external validation of the models. Furthermore, the class-imbalance problem in most of the medical image dataset has been solved by algorithm-level-techniques. Moreover, there is need to put more effort in combined detection and localization of pneumothorax as mostly research is limited to either classification or localization of pathology. So far, best results have been achieved by deep-learning based models with Area-underreceiver-operating-characteristic-curve (AUC) of 88.87% for classification, and Dice-similarity-coefficient (DSC) of 88.21% for localization of pneumothorax. Thus, the outstanding abilities of deep learning techniques can be deployed for developing robust models for pneumothorax detection.

show abstract

Fully Convolutional Multi-Scale ScSE-DenseNet for Automatic Pneumothorax Segmentation in Chest Radiographs

Cited by 8 publications

References 4 publications

Chest X-ray pneumothorax segmentation using U-Net with EfficientNet and ResNet architectures

Chest X-ray pneumothorax segmentation using U-Net with EfficientNet and ResNet architectures

Automated segmentation and diagnosis of pneumothorax on chest X-rays with fully convolutional multi-scale ScSE-DenseNet: a retrospective study

Automatic Diagnosis of Pneumothorax From Chest Radiographs: A Systematic Literature Review

Contact Info

Product

Resources

About